Method Of Generating An Optimized Ship Schedule To Deliver Liquefied Natural Gas

ABSTRACT

A system and method is provided for generating an optimized ship schedule to deliver liquefied natural gas (LNG) from one or more LNG liquefaction terminals to one or more LNG regasification terminals using a fleet of ships. The method involves modeling the ship schedule via an LNG ship scheduling model and a LNG ship rescheduling model to provide optimized decisions for the LNG supply chain. The LNG supply chain includes the one or more LNG liquefaction terminals, the one or more LNG regasification terminals, and the fleet of ships.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplications 62/020,890 filed Jul. 3, 2014 entitled METHOD OF GENERATINGAN OPTIMIZED SHIP SCHEDULED TO DELIVER LIQUEFIED NATURAL GAS; U.S.Provisional Patent Application 62/020,892 filed Jul. 3, 2014 entitledMETHOD OF GENERATING AN OPTIMIZED SHIP SCHEDULED TO DELIVER LIQUEFIEDNATURAL GAS; and U.S. Provisional Patent Application 61/990,035 filedMay 7, 2014 entitled METHOD OF GENERATION AN OPTIMIZED SHIP SCHEDULE TODELIVER LIQUEFIED NATURAL GAS, the entirety of which is incorporated byreference herein.

FIELD OF THE INVENTION

Disclosed aspects and methodologies relate to Liquefied Natural Gas(LNG) operations, and more particularly, to systems and methods relatingto planning and operations of an LNG project or projects.

BACKGROUND

This section is intended to introduce various aspects of the art, whichmay be associated with aspects of the disclosed techniques andmethodologies. References discussed in this section may be referred tohereinafter. This discussion, including the references, is believed toassist in providing a framework to facilitate a better understanding ofparticular aspects of the disclosure. Accordingly, this section shouldbe read in this light and not necessarily as admissions of prior art.

The current liquefied natural gas (LNG) business is driven by long-termcontracts and planning. Currently, annual delivery schedules for eachLNG project are planned and agreed upon by various parties before thebeginning of each contractual time period. In addition an updated 90-daydelivery schedule is developed by the LNG producer and provided tocustomers every month to account for deviations from the annualschedule. Agreement on these delivery plans can involve significantnegotiation and coordination of operations by several parties.Consequently, developing a portfolio of LNG projects and operating LNGliquefaction terminals involves significant long-term planning which cangreatly benefit from robust planning and optimization tools.

Increasing liquidity in the LNG market may cause the global LNG businessto evolve from a long-term contracts based business to one withsignificantly more flexibility and short-term sales. This willcomplicate the management of projects since operations will have to beoptimized not only to satisfy contractual obligations but also tomaximize profitability by exploiting contractual flexibility and marketopportunities. Known attempts to manage LNG projects via computationaltechnology have fallen short because of substantially reduced scope,reduced capabilities of the proposed solutions, and/or a lack of thetechnology utilized. The following paragraphs discuss known attempts asthey relate to various aspects of the disclosed methodologies andtechniques.

Many conventional LNG projects tend to use simple spreadsheets forscheduling ships. The schedule has to be populated manually and does notprovide any optimization functionality. Even in the more detailedsystems, there are no known integrated models for lifting schedulegeneration combined with ship schedule optimization. This can lead tosub-optimal plans manifested in over-utilization of spot vessels forsatisfying contractual demands. Further, generating a feasible shippingschedule could require a great number of iterations between the capacityplanning and the ship scheduling components. Additionally, the shipscheduling components of the more sophisticated models do not seek tooptimize schedules for selling spot cargoes, and do not account fortransportation losses in cargo (e.g. boil-off, fuel) and consequentlythe generated ship schedules have discrepancies when attempting tosatisfy contractual obligations related to annual volume delivered.

As an example, Rakke et al appears to describe a first attempt toaddress problems of developing Annual Development Plans (ADPs) forlarger LNG projects. See, e.g., J. G. Rakke, M. Stalhane, C. R. Moe, M.Christiansen, H. Andersson, K. Fagerholt, I. Norstad, (2010), “A rollinghorizon heuristic for creating a liquefied natural gas annual deliveryprogram”, To appear in Transportation Research Part C,doi:10.1016/j.trc.2010.09.006. While Rakke reports results for problemswith multiple ships and a one year planning horizon, the optimizationmodel and solution methods are fairly simplified. For example, the modelis built for a case with only one producing terminal, boil-off and heelcalculations are not integrated with ship schedules, partial loads anddischarges are not allowed, time windows are not specified fordeliveries, etc. From a practical perspective, known ship schedulemethodologies address a much simplified and a small subset of the LNGship schedule optimization problem.

To address some of the problems in conventional methods, other methodsmay provide the capability to perform a number of valuation andvalidation analyses for the LNG supply chain incorporating options andopportunities. See, e.g., Intl. Patent Application Publication Nos.2013/085688; 2013/085689; 2013/085690; 2013/085691; and 2013/085692.These methods may include identification and valuation of short-term andlong-term options, portfolio planning analysis, and management ofshipping operations, validation of supply chain operability, and new LNGproject design and evaluation. Accordingly, these references describe asuite of fit-for-purpose optimization and analytics applications areused in combination within various workflows and methodologies. Inparticular, these models form the combined suite of applications (e.g.,five optimization and analytical models in the software suite) to beused in operations, analysis and decision-making within the LNG valuechain. These models include: (1) ship scheduling, which has a capabilityfor combined LNG ship scheduling, logistics and inventory optimizationto develop annual delivery programs, rolling 90-day schedules, orschedules of any other useful scheduling time horizon; (2) optionalityplanning, which is used to identify the benefits, value or advantages inpotential options and investments in long-term global LNG marketanalysis and for portfolio planning; (3) price model, which providesadvanced price scenario generation capabilities enabling the valuationand statistical analysis of short-term optionality; (4) supply chaindesign, which provides optimization under uncertainty for robust LNGsupply chain designs of new LNG projects including appropriateoperational details; and (5) shipping simulation, which is ahigh-fidelity simulator to study, probabilistically analyze andvisualize the behavior of LNG supply chain operations. The modelsencompass a variety of analytical tasks and levels of fidelity.

Despite these enhancements, existing models for Annual Development Planfail to properly integrate each of the components, such as multipleproduction facilities, multiple storage facilities, multiple berths bothin production and regas side, multiple LNG grades, multiple ships invarying capacities and fuel options, multiple contracts/deliverylocations, mass balance calculations (production, inventory, andpotential losses), full, partial and co-loads, planned dry-dockschedules, ratability windows, complex delivery windows, and fiscalcalculations. Accordingly, a need exists to integrate each of thecomponents within a model that can optimize all variablessimultaneously. In addition, conventional algorithms do not optimizereal-sized problems in a time frame necessary for practical businesspurposes. In particular, what is needed is an algorithm and itsextensions that optimize real-sized models in a reasonable time frame.Further, conventional methods consider all constraints to be equallyimportant. Hence, for highly constrained systems, these methods may beunsuccessful in finding any solution due to their inherentinflexibility. As such, a need exists to be able to prioritizeconstraints such that some of them can be considered more important(“hard”), than others (“soft”). This will enable us to find solutionsthan can satisfy all hard (critical) constraints and minimize violationin the soft (less important) constraints. As a result, finding goodsolutions can become easier, with the possibility of improving economicsat the expense of minor violations in the soft constraints.

Further still, none of the conventional models provide the ability toupdate the ship schedules based on operational disruption events, or onupdated information that is different from the assumptions that wereused for developing the plan. This type of functionality is useful indeveloping 90-day delivery schedules. Because the annual deliveryschedules are negotiated between the LNG buyers and LNG sellers to bestsuit the interests of both parties, a need exists to develop updatedship schedules being able to re-optimize the schedules, such that thedeviations from the initial plan can be minimized, in addition tomaximizing economics.

SUMMARY

In yet another aspect, a computer implemented method for generating anoptimized ship schedule to deliver liquefied natural gas (LNG) from oneor more LNG liquefaction terminals to one or more LNG regasificationterminals using a fleet of ships is described. The method includes:obtaining a baseline ship schedule for LNG shipping operations;obtaining input data associated with the LNG shipping operations; inresponse to the input data, developing an updated ship schedule usingone or more algorithms, wherein the one or more algorithms balance theeconomics of the updated ship schedule and the changes in the updatedship schedule as compared to the baseline ship schedule; outputting oneor more notifications associated with the updated ship schedule; andusing the one or more notifications to perform LNG shipping operations.

The one or more algorithms comprises developing an updated ship schedulethat does not have constraint violations; minimizing deviation betweenthe updated ship schedule and the baseline ship schedule; maximizingeconomics while maintaining deviation between the updated ship scheduleand the baseline ship schedule within a tolerance; maximizing economicsand evaluating additional LNG shipment opportunities; minimizingdeviation between the updated ship schedule and the baseline shipschedule while maintaining the economics.

In still yet another aspect, a system for generating an optimized shipschedule to deliver liquefied natural gas (LNG) from one or more LNGliquefaction terminals to one or more LNG regasification terminals usinga fleet of ships is described. The system comprises a processor; aninput device in communication with the processor and configured toreceive input data associated with the LNG shipping operations; memoryin communication with the processor, the memory having a set ofinstructions, wherein the set of instructions, when executed, areconfigured to: obtain a baseline ship schedule for LNG shippingoperations; in response to the input data, develop an updated shipschedule using one or more algorithms, wherein the one or morealgorithms balance the economics of the updated ship schedule and thechanges in the updated ship schedule as compared to the baseline shipschedule; an output device that outputs one or more notificationsassociated with the updated ship schedule.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other advantages of the present invention may becomeapparent upon reviewing the following detailed description and drawingsof non-limiting examples of embodiments in which:

FIG. 1A is a block diagram of a LNG supply chain design optimizationmodel.

FIG. 1B is a flow chart of a LNG supply chain design optimization.

FIG. 2A is a flow chart of an LNG ship scheduling optimization methodaccording to an exemplary embodiment of the present techniques.

FIG. 2B is a flow chart of the five stage algorithm method for the LNGship scheduling model according to an exemplary embodiment of thepresent techniques.

FIG. 2C is a flow chart of the five stage algorithm with soft extensionsmethod according to an exemplary embodiment of the present techniques.

FIG. 3 is flow chart of a rolling time algorithm according to anexemplary embodiment of the present techniques.

FIG. 4 is a block diagram of a LNG ship rescheduling optimization modelfor use in a 90-day plan according to an exemplary embodiment of thepresent techniques.

FIG. 5A is a block diagram of how the system may be implemented for theLNG ship rescheduling model according to an exemplary embodiment of thepresent techniques.

FIG. 5B is a flow chart of the five stage algorithm method for the LNGship rescheduling model according to an exemplary embodiment of thepresent techniques.

FIG. 6 is a chart of a notional curve representing the incrementaleconomics generated by allowing for more changes in the updated schedulecompared to the baseline schedule.

FIG. 7 is a diagram that representations the difference in shipschedules in the updated and baseline plans.

FIG. 8 shows a chart of a notional comparison of economics for theupdated delivery plan compared to baseline delivery plan.

FIG. 9 is a diagram that represents the difference in ship schedules forhow cargos are moving between the ships.

FIG. 10 is another diagram that represents the difference in shipschedules for how cargos are moving between the ships.

FIG. 11 is a diagram that represents the difference in ship assignments.

FIG. 12 is another diagram that represents the difference in shipassignments.

FIG. 13 is a block diagram of a computing system according to disclosedaspects and methodologies.

DETAILED DESCRIPTION

To the extent the following description is specific to a particularembodiment or a particular use, this is intended to be illustrative onlyand is not to be construed as limiting the scope of the invention. Onthe contrary, it is intended to cover all alternatives, modifications,and equivalents that may be included within the spirit and scope of theinvention.

Some portions of the detailed description which follows are presented interms of procedures, steps, logic blocks, processing and other symbolicrepresentations of operations on data bits within a memory in acomputing system or a computing device. These descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. In this detailed description, a procedure,step, logic block, process, or the like, is conceived to be aself-consistent sequence of steps or instructions leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, although not necessarily, these quantities take theform of electrical, magnetic, or optical signals capable of beingstored, transferred, combined, compared, and otherwise manipulated. Ithas proven convenient at times, principally for reasons of common usage,to refer to these signals as bits, values, elements, symbols,characters, terms, numbers, or the like.

Unless specifically stated otherwise as apparent from the followingdiscussions, terms such as generating, modeling, accepting, interfacing,running, outputting, evaluating, optimizing, performing, minimizing,maximizing, developing, determining, analyzing, identifying,representing, incorporating, entering, employing, displaying, using,integrating, simulating, valuating, valuing, validating, comparing,accounting for, prescribing, or the like, may refer to the action andprocesses of a computer system, or other electronic device, thattransforms data represented as physical (electronic, magnetic, oroptical) quantities within some electrical device's storage into otherdata similarly represented as physical quantities within the storage, orin transmission or display devices. These and similar terms are to beassociated with the appropriate physical quantities and are merelyconvenient labels applied to these quantities.

Embodiments disclosed herein also relate to an apparatus for performingthe operations herein. This apparatus may be specially constructed forthe required purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program or codestored in the computer. Such a computer program or code may be stored orencoded in a non-transitory computer readable medium or implemented oversome type of transmission medium. A computer-readable medium includesany medium or mechanism for storing or transmitting information in aform readable by a machine, such as a computer (‘machine’ and ‘computer’are used synonymously herein). As a non-limiting example, anon-transitory computer-readable medium may include a computer-readablestorage medium (e.g., read only memory (“ROM”), random access memory(“RAM”), magnetic disk storage media, optical storage media, flashmemory devices, etc.). A transmission medium may be twisted wire pairs,coaxial cable, optical fiber, or some other suitable transmissionmedium, for transmitting signals such as electrical, optical, acousticalor other form of propagated signals (e.g., carrier waves, infraredsignals, digital signals, etc.)).

Furthermore, modules, features, attributes, methodologies, and otheraspects can be implemented as software, hardware, firmware or anycombination thereof. Wherever a component of the invention isimplemented as software, the component can be implemented as astandalone program, as part of a larger program, as a plurality ofseparate programs, as a statically or dynamically linked library, as akernel loadable module, as a device driver, and/or in every and anyother way known now or in the future to those of skill in the art ofcomputer programming. Additionally, the invention is not limited toimplementation in any specific operating system or environment.

Example methods may be better appreciated with reference to flowdiagrams. While for purposes of simplicity of explanation, theillustrated methodologies are shown and described as a series of blocks,it is to be appreciated that the methodologies are not limited by theorder of the blocks, as some blocks can occur in different orders and/orconcurrently with other blocks from that shown and described. Moreover,less than all the illustrated blocks may be required to implement anexample methodology. Blocks may be combined or separated into multiplecomponents. Furthermore, additional and/or alternative methodologies canemploy additional blocks not shown herein. While the figures illustratevarious actions occurring serially, it is to be appreciated that variousactions could occur in series, substantially in parallel, and/or atsubstantially different points in time.

Various terms as used herein are defined below. To the extent a termused in a claim is not defined below, it should be given the broadestpossible definition persons in the pertinent art have given that term asreflected in at least one printed publication or issued patent.

As used herein, “and/or” placed between a first entity and a secondentity means one of (1) the first entity, (2) the second entity, and (3)the first entity and the second entity. Multiple elements listed with“and/or” should be construed in the same fashion, i.e., “one or more” ofthe elements so conjoined.

As used herein, “displaying” includes a direct act that causesdisplaying, as well as any indirect act that facilitates displaying.Indirect acts include providing software to an end user, maintaining awebsite through which a user is enabled to affect a display,hyperlinking to such a website, or cooperating or partnering with anentity who performs such direct or indirect acts. Thus, a first partymay operate alone or in cooperation with a third party vendor to enablethe reference signal to be generated on a display device. The displaydevice may include any device suitable for displaying the referenceimage, such as without limitation a CRT monitor, a LCD monitor, a plasmadevice, a flat panel device, or printer. The display device may includea device which has been calibrated through the use of any conventionalsoftware intended to be used in evaluating, correcting, and/or improvingdisplay results (e.g., a color monitor that has been adjusted usingmonitor calibration software). Rather than (or in addition to)displaying the reference image on a display device, a method, consistentwith the invention, may include providing a reference image to asubject. “Providing a reference image” may include creating ordistributing the reference image to the subject by physical, telephonic,or electronic delivery, providing access over a network to thereference, or creating or distributing software to the subjectconfigured to run on the subject's workstation or computer including thereference image. In one example, the providing of the reference imagecould involve enabling the subject to obtain the reference image in hardcopy form via a printer. For example, information, software, and/orinstructions could be transmitted (e.g., electronically or physicallyvia a data storage device or hard copy) and/or otherwise made available(e.g., via a network) to facilitate the subject using a printer to printa hard copy form of reference image. In such an example, the printer maybe a printer which has been calibrated through the use of anyconventional software intended to be used in evaluating, correcting,and/or improving printing results (e.g., a color printer that has beenadjusted using color correction software).

As used herein, “exemplary” is used exclusively herein to mean “servingas an example, instance, or illustration.” Any aspect described hereinas “exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects.

As used herein, “hydrocarbon” includes any of the following: oil (oftenreferred to as petroleum), natural gas in any form including liquefiednatural gas (LNG), gas condensate, tar and bitumen.

As used herein. “machine-readable medium” refers to a non-transitorymedium that participates in directly or indirectly providing signals,instructions and/or data. A machine-readable medium may take forms,including, but not limited to, non-volatile media (e.g. ROM, disk) andvolatile media (RAM). Common forms of a machine-readable medium include,but are not limited to, a floppy disk, a flexible disk, a hard disk, amagnetic tape, other magnetic medium, a CD-ROM, other optical medium, aRAM, a ROM, an EPROM, a FLASH-EPROM, EEPROM, or other memory chip orcard, a memory stick, and other media from which a computer, a processoror other electronic device can read.

The terms “optimal,” “optimizing,” “optimize,” “optimality,”“optimization” (as well as derivatives and other forms of those termsand linguistically related words and phrases), as used herein, are notintended to be limiting in the sense of requiring the present inventionto find the best solution or to make the best decision. Although amathematically optimal solution may in fact arrive at the best of allmathematically available possibilities, real-world embodiments ofoptimization routines, methods, models, and processes may work towardssuch a goal without ever actually achieving perfection. Accordingly, oneof ordinary skill in the art having benefit of the present disclosurewill appreciate that these terms, in the context of the scope of thepresent invention, are more general. The terms may describe one or moreof: 1) working towards a solution which may be the best availablesolution, a preferred solution, or a solution that offers a specificbenefit within a range of constraints; 2) continually improving; 3)refining; 4) searching for a high point or a maximum for an objective;5) processing to reduce a penalty function; or 6) seeking to maximizeone or more factors in light of competing and/or cooperative interestsin maximizing, minimizing, or otherwise controlling one or more otherfactors, etc.

As used herein, the term “production entity” or “production entities”refer to entities involved in a liquefaction project or regasificationproject.

Example methods may be better appreciated with reference to flowdiagrams. While for purposes of simplicity of explanation, theillustrated methodologies are shown and described as a series of blocks,it is to be appreciated that the methodologies are not limited by theorder of the blocks, as some blocks can occur in different orders and/orconcurrently with other blocks from that shown and described. Moreover,less than all the illustrated blocks may be required to implement anexample methodology. Blocks may be combined or separated into multiplecomponents. Furthermore, additional and/or alternative methodologies canemploy additional blocks not shown herein. While the figures illustratevarious actions occurring serially, it is to be appreciated that variousactions could occur in series, substantially in parallel, and/or atsubstantially different points in time.

In general, LNG inventory routing problems (IRPs) have specialcharacteristics that differentiate them from general maritime IRP's.Maritime IRP's in turn have special characteristics that distinguishthem from vehicle routing problem (VRPs). The LNG IRP is based on areal-world application and shares the fundamental properties of a singleproduct maritime IRP. However, the LNG IRP includes several variationsincluding variable production and consumption rates, LNG specificcontractual obligations, and berth constraints. Further, the LNG IRPseeks to generate schedules where each ship perform several voyages overa time horizon with both the number of voyages and the time horizonbeing considerably larger than those considered by a typical maritimeIRP. As an example, LNG boils off during transportation and thisboil-off can be used as fuel. Further, LNG involves long term deliverycontracts where schedules for a year have to developed and then adheredto as closely as possible. In LNG shipping, the ships are typicallyfully loaded and fully discharged and the ships are owned/leased byshipper, which further complicate the economics related to taxes,royalties, etc.

The present techniques involve enhancements to systems and methods forship schedule optimization. The present techniques involve integratingLNG ship schedule optimization and inventory management in an enhancedmanner. As may be appreciated, various inputs, such as productionschedule of different grades of LNG, inventory limitations, ship fleetdetails and ship-terminal compatibility, contract details includingdestinations, quantity, pricing, ratability requirements are integratedto enhance LNG shipping operations. As part of the LNG shippingoptimization algorithm, several search routines may be utilized tooptimize shipping decisions, e.g., which ship to deliver which cargo toa certain destination. Typically, the optimal solution is searched for acriterion such as maximizing social economics, minimizing shipping cost.Exemplary ship schedule optimization methods and systems are describedin Intl. Patent Application Publication Nos. 2013/085688; 2013/085689;2013/085690; 2013/085691; and 2013/085692, each of which is incorporatedhereby by reference in their entirety.

As shown in FIG. 1A, an LNG optimization model 102 may obtain variousinputs, such as production terminal information 104 (e.g., productionschedule and/or infrastructure constraints), ship fleet information 106(e.g., terminal compatibility, boil-off rates, fuel options, and/ordegree of pooling), delivery contract information 108 (e.g.,destination, annual quantity, ratability, pricing and/or optionality),and fiscal information 110 (e.g., tax and/or royalty structure). Theseinputs are used by the LNG shipping optimization model 102 to generatevarious outputs that are used in the LNG shipping operations. Theseoutputs include shipping decision information 112 (e.g., ship schedule,optimal fleet size, fuel requirements, speed selection, maintenanceschedule); terminal inventory profile 114 (e.g., amount of inventory fora given time period); optionality information 116 (e.g., diversions);and profitability metrics 118 (e.g., taxes, royalty, expenses,allocation of income, etc.).

As LNG projects include one or more LNG production or liquefactionterminals that supply LNG to multiple regasification terminals using afleet of ships, the outputs of the LNG shipping optimization model 102generates a schedule and associated plans for the LNG shippingoperations. The outputs include an annual delivery plan (ADP) created tospecify the LNG delivery schedule for the forthcoming planning period(e.g., year) for one or more customers. The ADP may be developed andagreed upon by the supplier and the various customers. In addition, oneor more 90-day plans (e.g., delivery schedule that accounts fordeviations in the existing business conditions from the forecasts usedduring the ADP development) is provided by the LNG shipping optimizationmodel 102. This 90-day plan may be provided to one or more customers ona monthly basis through the year.

The present techniques involve enhancement to a method and system todevelop annual delivery plans (ADPs) and some of which apply todeveloping 90-day schedules (90DS). Specifically, the system enablesoptimization of ship schedules, terminal inventory management, LNGproduction schedules, and maintenance schedules while accounting fortradeoffs related to various options in available shipping, customerrequirements, price uncertainty, contract flexibility, marketconditions, and the like. The system may provide the ability to optimizethese decisions from several perspectives including minimizing costs,satisfying contractual obligations, maximizing profit, and the like.

The method and system includes specific workflows, which use variousoptimization models to provide enhanced ADP and 90-day plans. As anexample, the system may include models and algorithms stored in memory,input data and output data stored in one or more databases, and agraphical user interface.

For ADP creation, the disclosed aspects and methodologies provide thecapability to perform a number of valuation and validation analyses forthe LNG supply chain incorporating options and opportunities. Inparticular, one or more embodiments may be include a system or methodthat enhances LNG ship scheduling modeling (e.g., ADP modeling), suchthat the LNG ship scheduling model is configured to (1) obtain inputdata associated with LNG shipping operations; (2) determine objectivesfor the LNG shipping operations; (3) define constraints for theoptimization decisions, wherein the constraints include hard constraintsand soft constraints; (4) determine one or more algorithms, wherein thealgorithms include basic algorithms and along one or more extensions tohandle soft constraints; (5) calculate optimal decisions using the oneor more algorithms to maximize and/or minimize one or more objectivesbased on the input data, one or more soft constraint and one or morehard constraint; and (6) generate output data based on decision data.Then, the LNG ship schedule or updated ADP may be utilized to operatethe LNG supply operations.

For the LNG ship scheduling (e.g. ADP), the data used in the modelingmay include one or more of contract specification; production andre-gasification location specifications rates; ship specifications; shipcompatibilities with contracts, production and re-gas terminals and/orberths; dry-dock requirements; storage tank requirements; berthspecifications; berth and storage maintenance; loans and/or transfers ofLNG among co-located joint ventures; LNG grades; optionalityopportunities including in-charter, out-charter and/or diversion; priceprojections; and potential ship routes. The output data may include shiptravel details (e.g., time, route, fuel, speed, “state” (e.g., warm orcold)); cargo size; LNG quality; storage-berth combination which a cargois served; ship status as in-chartered or out-chartered; and planneddry-dock for the ship. The constraints may include one or more of shiptravel restrictions based on arrival and departure times, speed,distance, loading/unloading operations; compatibility requirementsbetween different parties, storage tanks, berths, contracts, cargosizes; limits on loans between different parties; storage restrictions;berth utilization; contractual requirements (quantity, time); and/ormaintenance requirements. The objectives may include one or more ofmaximize social interest; minimize shipping costs; maximizeprofitability; and/or maximize shareholder profitability (e.g., multipledifferent parties involved in operations).

The basic algorithms may include different algorithms that may beutilized in the system. As an example, the algorithms may be configuredto be a five-stage method. The five stage method may include (i) stage1: generating feasible solution to the feasibility model; (ii) stage 2:generating optimal solution to feasibility model; (iii) stage 3:reducing in-chartered ships; (iv) stage 4: pursuing out-charteropportunity; and (v) stage 5: finding optimal solution to optimalitymodel. The feasibility model, in-charter model, out-charter model andoptimality model is based on the LNG ship scheduling model or is avariant of the LNG ship scheduling model (e.g., an embodiment of the ADPmodel). These models are a less complex models that are computationalmore efficient than the LNG ship scheduling model.

The extensions configured to handle soft constraints may include a softratability constraint method and a soft inventory constraint method. Thesoft ratability constraint method may include different approaches, suchas minimizing violations after stage 5 of the five-stage method and/orminimizing violations after stage 2 of the five-stage method. The softinventory constraint method may also include different approaches, suchas minimizing violations after stage 5 of the five-stage method and/orminimizing violations after stage 2 of the five-stage method.

Beneficially, the present techniques provide an annual development planthat integrates each of the components, such as multiple productionfacilities, multiple storage facilities, multiple berths both inproduction and regas side, multiple LNG grades, multiple ships invarying capacities and fuel options, multiple contracts/deliverylocations, mass balance calculations (production, inventory, andpotential losses), full, partial and co-loads, planned dry-dockschedules, ratability windows, complex delivery windows, and fiscalcalculations. Further, the present techniques may be utilized tooptimize real-sized problems in a time frame necessary for practicalbusiness purposes. The present techniques may be utilized to handle softconstraints that are effective in finding feasible solutions easier orimproving economics. As another enhancement, the present techniques mayprovide updates to ship schedules based on operational disruptionevents, or on updated information that is different from the assumptionsthat were used for developing the plan, and/or on customer requests fordelivery changes, and/or on new market opportunities. This functionalitymay be utilized to enhance the 90-day delivery schedules (e.g., the90-day plan), schedule repair, and/or ADP negotiation. Also, the presenttechniques may be configured to re-optimize schedules such that thedeviations from the initial plan can be minimized, in addition tomaximizing economics.

As an example, FIG. 1B is a flow chart 150 of a LNG supply chain designoptimization. In this flow chart 150, the generation and use of the ADPis described in blocks 152 to 156, while the generation and use of the90-day plan is described in blocks 158 to 164.

With regard to the LNG ship scheduling model (e.g., ADP model), inputdata on LNG shipping operations is obtained in block 152. This inputdata may include the input data described in blocks 104 to 110 of FIG.1A. From this input data, an ADP may be generated from the LNG shipscheduling model, as shown in block 154. The generation of the LNG shipschedule model is described further below with reference to FIGS. 2A to3 and 13. The ADP may be iterated through various versions based onchanges and/or discussions with third parties. The ADP may be displayedand used for LNG shipping operations as shown in block 156. The displayof the ADP may be via a computer display, and use may involve using theADP for LNG shipping operations.

With regard to the LNG ship re-scheduling model (e.g., 90-dayscheduling, schedule repair, ADP negotiation), input data on LNGshipping operations may be obtained in block 161. This input data mayinclude the input data described in blocks 104 to 110 of FIG. 1A orother data, as noted further below. A baseline schedule may also beobtained in block 160. The baseline schedule can be user-defined basedon the ADP produced by the LNG ship scheduling model, as discussed inblock 154, or the updated plan produced by the LNG ship reschedulingmodel, as discussed in block 158. At block 158, a 90-day plan may begenerated from the LNG ship re-scheduling model. The generation of the90-day plan may include obtaining input data on LNG shipping operations,as discussed in block 161, and/or obtaining an LNG operation baselineschedule, as shown in block 160. The LNG ship re-scheduling model isdescribed further below with reference to FIGS. 4 to 13. The 90-day planmay be displayed and used for LNG shipping operations, as shown in block162. The display of the 90-day plan may be via a computer display, anduse may involve using the 90-day plan for LNG shipping operations.Further, as shown in block 164, updated information may be obtained.This updated information may be utilized to construct the baselineschedule for the next ship schedule updating. The present techniques areexplained further below in FIGS. 2A to 13.

LNG Ship Scheduling Optimization Model

As noted above, the present techniques integrate LNG ship scheduleoptimization and inventory management. The LNG shipping optimizationmodel uses various inputs, such as production schedule of differentgrades of LNG, inventory limitations, ship fleet details andship-terminal compatibility, contract details including destinations,quantity, pricing, ratability requirements. Then, optimizationalgorithms are used to optimize shipping decisions (e.g., which ship todeliver which cargo to a certain destination). The output (e.g.,decisions) is based on the optimal solution to an objective function,which may include maximizing social economics and/or minimizing shippingcost.

FIG. 2A is a flow chart 200 of an ADP optimization method according toan exemplary embodiment of the present techniques. In this diagram 200,various steps may be performed to create an ADP based on an LNG shipscheduling model. At block 202, input data associated with LNG shippingoperations is obtained to for use in a LNG ship scheduling model. Theinput data may include data associated with the long-term planningoperations. In addition, the input data may be categorized as:production data at one or more liquefaction terminals, facilitymanagement data at production terminals, customer requests, customerterminal data, market conditions, contracts and/or shipping data.Production data may include data relating one or more of: the types orgrades of LNG produced and their heat content; production rates of oneor more types or grades of LNG; production unit maintenance schedulesand associated flexibility in scheduling the maintenance. Facilitymanagement data may include data relating one or more of: the number ofberths available for loading; storage capacity for each type or grade ofLNG; connection between berth and storage unit; and berth maintenanceschedules and their associated flexibility. Customer requests mayinclude input data relating to one or more of the ratability ofdeliveries; the time window and cargo sizes for specific deliveries; thespeeds that the ships should take; and the fuel modes that the shipsshould use. Customer terminal data may include input data relating toone or more of: storage capacities for each type or grade of LNG; thenumber of berths available for unloading; regasification rate schedules;and distances from each liquefaction terminal. Market conditions mayinclude input data relating to one or more of: the outlook for indexprices to be used in pricing formula; the outlook for future marketopportunities such as spot sales; and futures and forward contractprices. Contracts may include input data relating to one or more of:terminals where LNG can be delivered; annual delivery targets for eachcustomer terminal; ratability of delivery, which is the timing andspacing of delivery of portions of an agreed-upon amount of LNG; gasquality, type, or grade to be delivered; pricing formulas; diversionflexibility; and other types of flexibility such as downflex (an optionwhereby the buyer may request a decreased quantity of LNG). Contractsmay also include input data relating to the length of contract to whichone or more LNG customers are bound. For example, an LNG customer may bebound by a long-term contract, such as sales and purchase agreements ora production sharing contracts. Shipping data may include input datarelating to one or more of: a list of leased DES (delivered ex ship),CIF (cost, insurance and freight), CFR (cost and freight) and availablespot ships; a list of FOB (freight on board) ships for each customer,the ships typically being owned or leased by the customer, shipcapacities; restrictions on what ship can load/unload at what terminal;maintenance schedules for ships; cost structures for ships; boil-off andheel calculations for each ship, including an optimal heel amount upondischarge at a regasification terminal; range of ship speeds andassociated cost profile; and/or set of allowed fuels. Further, the inputdata may include one or more of contract specification; production andre-gasification location specifications rates; ship specifications; shipcompatibilities with contracts, production and re-gas terminals and/orberths; dry-dock requirements; storage tank requirements; berthspecifications; berth and storage maintenance; loans and/or transfers ofLNG among co-located joint ventures; LNG grades; optionalityopportunities including in-charter, out-charter and/or diversion; priceprojections; and potential ship routes.

At block 204, objectives for the LNG shipping operations are determined.The objectives may be determined by the user or system to providecertain outputs. The objectives may include one or more of maximizesocial interest; minimize shipping costs; maximize profitability; and/ormaximize shareholder profitability (e.g., multiple different partiesinvolved in operations).

Then, at block 206, constraints for the optimization decisions aredefined. The constraints may include hard constraints and softconstraints. The constraints may include one or more of ship travelrestrictions based on arrival and departure times, speed, distance,loading/unloading operations; compatibility requirements betweendifferent parties, storage tanks, berths, contracts, cargo sizes; limitson loans between different parties (e.g., JVs); storage restrictions;berth utilization; contractual requirements (quantity, time); and/ormaintenance requirements.

At block 208, one or more algorithms are determined. The algorithms mayinclude basic algorithms and along one or more extensions to handle softconstraints. The basic algorithms may include different algorithms thatmay be utilized in the system. As an example, the algorithms may beconfigured to be a five-stage method. The five stage method may include(i) stage 1: generating feasible solution to the feasibility model; (ii)stage 2: generating optimal solution to feasibility model; (iii) stage3: reducing in-chartered ships; (iv) stage 4: pursuing out-charteropportunity; and (v) stage 5: finding optimal solution to optimalitymodel.

Once the algorithms, constraints and objectives are determined, optimaldecisions are calculated, as shown in block 210. The calculation ofoptimal decisions may include using the one or more algorithms tomaximize and/or minimize one or more objectives based on the input data,one or more constraint that may be confirguable as a soft or hardconstrant (e.g., turned into a soft constraint or maintained as a hardconstraint). This may be subject to a model including input data, one ormore constraint that may be adjusted based on the various settings. Theextensions is configured to handle soft constraints may include a softratability constraint method and a soft inventory constraint method. Thesoft ratability constraint method may include different approaches, suchas minimizing violations after stage 5 of the five-stage method and/orminimizing violations after stage 2 of the five-stage method. The softinventory constraint method may also include different approaches, suchas minimizing violations after stage 5 of the five-stage method and/orminimizing violations after stage 2 of the five-stage method.

Once calculated, the optimal decisions are provided as output data, asshown in block 212. The output data may include ship travel details(e.g., time, route, fuel, speed, “state” (e.g., warm or cold)); cargosize; LNG quality; storage-berth combination which a cargo is served;ship status as in-chartered or out-chartered; and planned dry-dock forthe ship.

Then, the output is utilized for LNG operations, as shown in block 214.The use of the output may include updating the ADP or other schedulingplan, adjusting may be utilized to operate the LNG supply operations.That is, the decision variables are used by the user to manage and/oradjust LNG shipping operations.

As noted above, the LNG ship scheduling model may include various typesof input data, constraints, and/or objectives. The LNG ship schedulingmodel may be a mathematical model that involves a collection ofindependent decision variables, dependent (auxiliary) variables, andconstraints specified by a data input instance. Decision variables maybe manipulated by the solution algorithm toward the optimal value of anobjective function (e.g. to find optimal decisions). The final solutionis assumed to satisfy the associated constraints and/or limits.

As an example, the LNG ship scheduling model may include an initialstate of the inventories, availability of ships, projected productionprofiles, contractual requirements, such as target quantity and/orquality; and schedules cargos in varying sizes to ships such that anobjective, such as social economics, is optimized. The LNG shipscheduling model can be used for generating an initial annual deliveryplan (ADP). In addition, the model can be used to analyze the impact ofvarious business decisions to the bottom-line economics. One specificexample may involve reviewing the impact of speeding up (or down) theentire or a part of the fleet and also determining whether toout-charter or in-charter a ship.

As this is a mathematical model, input data for the LNG ship schedulingmodel includes a wide range of data types. Some or all of this data maybe required for a specific instance. This section presents all the inputdata and provides further explanation for certain sections. As anexample, the time horizon may be one of the input data types. The timehorizon may include the start date of the planning horizon; length andduration (e.g., a one year period, but it may involve shorter and/orlonger periods); time discretization, which is the smallest unit of timethat specifies the frequency of decisions (e.g., 2 days, 1 day, and ½day). The time discretization is useful because the time calculationsare based on discrete units of time. Fine discretization of timeincreases the complexity of the calculations by increasing the size andvolume of computations, while course discretization may not providerealistic schedules as it is a simplified view of the problem. The timediscretization is assumed to uniform throughout the planning horizon.

Due to the time discretization, rounding of various time-based data maybe performed. The rounding rules employed may involve an internalparameter, such as X. Any time-based parameter or data may be roundeddown to the nearest integer number of time units, if the fraction isless than or equal to X, and rounded up to the next largest integernumber of time units, if fraction is greater than X. As a specificexample, if X is equal (=) to 0.3 with discrete time units of one day,then a value of 1.25 days is rounded down to 1 day and 4.6 days isrounded up to 5 days. Further, some data may be the sum of multiple dataentries (e.g., input data) and then the sum is rounded up or down. Assuch, for the discrete time units greater than a day, the data may beaggregated. For discrete time units less than a day, daily data will bedivided evenly. Price data is simply multiplied or reduced based on timegranularity (e.g. half day units have same price where price is givendaily). Further, to accommodate multi-time period operations, such asloading and unloading, the quantity loaded or unloaded per day mayinvolve an approximation. The load or unload quantity may be spread outevenly across the number of time units associated with loading and/orunloading and post-loading and/or unloading activities. Time unitrounding rules may be used to calculate that number of time units.

Another data type that may be utilized with the LNG ship schedulingmodel is ship specification data. The ship specification data may beassociated with each ship in the fleet. The ship specification data mayinclude ship type; owner, capacity; cargo sizes; fuel options; fuel(diesel) usage; daily boil-off rates; minimum SSD MCR coefficient;minimum DFD MCR coefficient; heel volume; fuel usage at port and canal;speed; fixed cost; available time windows; start location and state, endlocation and state; out-charter option; cool-down time (e.g., cool-downtime from warm and/or cool-down time from dry); and/or cool down lossfrom warm; cool down loss from dry.

The ship specification data may include information about the ships'operation. This data may include classifications, such as conventional,QFLEX, QMAX). The owner may include a designation whether the ship isjoint venture ship (JV), which is referred to as DES, and if it is acustomer ship, it is referred to as FOB. The capacity may includemaximum volume of LNG that a ship can carry and/or ability to load anyquality and/or grade of LNG. The cargo sizes may include discrete set ofloading sizes for ships, which may not necessarily be used in allvariants of the LNG ship scheduling model, or more specifically an ADPmodel.

Another type of ship specification data is the fuel options. The fueloptions may include an indication that ships can operate using differentfuels. Further, as LNG is carried in very low temperatures, some of theLNG evaporates (i.e. boil-off) during the travel. This is a result ofthe heat difference between the tank and outside and associatedefficiencies for the storage. Accordingly, some ships may use thisboil-off gas as fuel. This makes the fuel options and boil-off ratesrelated to each other. The fuel options may include this input tospecify which fuel options are compatible with each ship. For example,the input may be specified as NBO (natural boil-off) and diesel (e.g.,uses the boil-off from LNG tank as fuel in addition to the diesel fuel);NBO and forced boil-off (FBO) (e.g., uses natural and forced boil-off asfuel); slow steam diesel (SSD) with re-liquefaction (e.g., uses onlydiesel and liquefies the boil-off therefore there is no loss due toboil-off); and/or gas combustion unit (GCU) (e.g., uses only diesel,re-liquefaction is turned-off).

Another type of ship specification data may include other fuel relateddata types. For example, the fuel (diesel) usage may include a cubicfunction of speed, wherein its coefficients are provided or specified bythe ship manufacturer. The daily boil-off rates may depends on ship,voyage leg (loaded and/or ballast), speed, fuel selection. The minimumslow steam diesel (SSD) maximum continuous rating (MCR) coefficient isthe minimum MCR coefficient for SSD and steaming ships for calculatingfuel use based on speed. The minimum DFD MCR coefficient is the minimumMCR coefficient for DFD ships for calculating fuel use based on speed.The heel volume involves the ship's requirement of a small amount of gasleft in the tank in the ballast leg of a ship's trip to keep the tankcool by natural evaporation. Ships are assumed to arrive at a productionterminal in cold state. Fuel usage at port and canal is the ship's useof some fuel while they are not travelling. An average daily fuelconsumption rate is assumed.

The ship specification data may also include ship operation data. Forexample, the speed may include the ship's speed, which is assumed totravel with one of three average speeds (e.g., minimum, maximum, andnominal). This type of discretization is useful to formulate the LNGship scheduling model using a time-space network. The travel times areassumed to be identical during the entire year for this aspect. The LNGship scheduling model can also accommodate speed changes based onseasonal variations, if preferred. As specific example, the LNG shipscheduling model may include MCR at a base speed, which is the maximumcontinuous rate at base speed. The fixed cost may include the fixeddaily cost incurred for each DES ship. This part of the shipping cost isfixed, while the fuel cost depends on the distance and speed. Also, theavailable time windows are the time period that the ship is assumed tobe available. The unavailability due to dry-dock may be handledseparately. The start location and state, end location and state areinitial conditions that are set for a ship based on last year's expectedfinal situation and business expectations for next year. The out-charteroption may specify if a DES ship can be considered for a potentialout-charter opportunity.

Further, the ship specification data may include additional shipoperation data. For example, the cool-down time is the time needed forthe ship to be cooled down before loading operation. The cool-down timefrom warm is the time spent when the ship arrives to production withoutany heel. The cool-down time from dry is the time that is spent when aship arrives to production from dry-dock. These can be ship, berth, orport specific. Also, the cool down loss from warm may include energycost to cool ship from warm and cool down loss from dry which is energycost to cool ship from dry.

As an example of the ship specification data, the following tabledescribes the comparison of boil-off ship and reliquefaction ship, asshown in Table 1. Table 1 summarizes the fuel, boil-off and heel relatedinformation, which is shown below:

TABLE 1 Boil-Off Ship Reliquefaction Ship LNG →Regas (NBO + FBO) vs.Reliq vs. GCU (i.e. burn) (NBO + Fuel Oil) Regas →LNG Cold (NBO + FBO)vs. Reliq vs. GCU (i.e. burn) (NBO + Fuel Oil) Regas →LNG Warm Fuel Oil(heel out) Fuel Oil (heel out)

As shown in this Table 1, the LNG ship scheduling model may includeconstraints that capture the relationship between speed, fuel usage,boil-off, and voyage leg for each ship.

Further still, the input data may include other specifications, such asproduction and regasification specifications and dry-dockspecifications. For example, the production and regasificationspecifications may include locations, compatibility with each vesseland/or port fees. The dry-dock specifications may include plannedmaintenance (e.g., each ship needs to be regularly maintained). Theplanned maintenance window may be included in the input data, which mayinclude the following specifications of locations, compatibility witheach vessel (e.g., dry-dock location; earliest and/or latest start date,duration, and/or exit status (e.g., cold, warm, dry, and the like)).

Another input data type may include storage tanks located in productionand re-gasification side; berths and storage and berth compatibility.The storage tanks located in production and re-gasification side mayinclude location, capacity, initial inventory and/or grade. The grademay include storage units at production terminals may only accept asingle quality of LNG, while the initial inventory is the amount of LNGavailable in the beginning of planning horizon. The storage unit may beshared by various parties. For example, in a joint venture (JV) setting,each party of the JV may send produced LNG to multiple storage units.These specifications have to comply with the compatibility between thejoint venture agreement and storage unit. The berths may includelocation; berthing/de-berthing time (e.g., amount of time a ship takesfor these operations); and/or load/unload rates (e.g., this rate affectsthe time it takes to complete loading or unloading operations). Therates and times may vary based on ship and location. The loading and/orunloading operation(s) may be performed at a single berth. The LNG shipscheduling model may or may not consider berth limits at re-gasificationterminals because a producer has limited visibility or control ofoperations at re-gasification terminals. The storage and berthcompatibility may involve specifying storage tanks that can transfer LNGto specific berths. This is typically bound by the availableinfrastructure of the LNG facility. For regas operations, the LNG shipscheduling model may be used for managing inventory for approximatingratability, actual control, simulating customer behavior.

The input data type may also include specific LNG information. Forexample, the input data may include loans and/or transfers of LNG amongco-located third parties (e.g., JVs). The loans and/or transfers of LNGamong co-located third parties may include third party supplier andstorage unit; destination third party (e.g., JV) and storage unit;quantity and quality of LNG; transfer rate, daily max loan and/ormaximum net; and/or time and/or date of transaction. Transfers may beassumed to finalize instantaneously within a storage unit. Betweenseparate storage units, transfer time may be based on a rate limitbetween units. Transfers should be of the same quality and/or grade. Thebalance should be restored by the end of the time horizon and no cost orfees are assumed for the transfers and loans. Also, production andregasification rates are another input data type. This data type mayinclude daily projections of LNG production of multiple third parties,allocation of a third parties production to a storage unit; and/or dailyprojections of re-gasification of customers. The daily production andre-gas rate forecasts are provided as input data. Production changes dueto planned maintenance can be adjusted in the input data. Moreover,another input data type may include LNG grades. The LNG grades mayinclude energy content: typically specified as energy quantity pervolume; boil-off equivalent: coefficient to calculate boil off fuelenergy equivalence to fuel oil amount; and/or density.

As another input data type, contractual data may be used in the LNG shipscheduling model. The contractual data may include contract name; thirdparty involved in the agreement (e.g., JVs, which may include single JVcustomer or multiple location assignment per contract); customer,location, type (e.g., DES/FOB), minimum (min), target, and/or maximum(max) delivery quantities, start and/or end date of annual deliverywindow, minimum and/or maximum energy content of delivered cargos;and/or ratability specifications (e.g., monthly, quarterly, etc.). Theratability time windows may be inclusive of deliveries within the timerange. These may include start date (e.g., beginning of ratabilityperiod); end date (e.g., end of ratability period); minimum quantity(e.g., minimum quantity in period); target quantity (e.g., targetquantity in ratability period); and/or maximum quantity (e.g., maximumquantity in ratability period).

The contractual data may also include various other contractual terms,such as fuel compatibility; whether or not an in-charter ship maydeliver one or more cargos for contract; whether or not vessels mayreturn back to supply location warm or cold; maximum additional spotsale; diversion notification time; maximum divertible quantity;replacement penalty; and/or compatible cargo sizes for each contract.The fuel compatibility may specify if ships serving a contract may use aspecific fuel type. The maximum additional spot sale may include themaximum amount of spot sales (e.g. due to overproduction) that may bedelivered to a contract. The diversion notification time may include anotification requirement for scheduling a diversion. The max divertiblequantity may include the maximum quantity that may be diverted fromcontract. The replacement penalty may include a fee paid to buyer fordiverted quantity. The compatible cargo sizes for each contract mayspecify the cargo sizes that may be utilized in the LNG ship schedulingmodel. Further, a contract can have multiple delivery windows, which mayor may not overlap and each cargo can have varying grades of LNG.

As yet another type of input data, time windows and maintenance data maybe used by the LNG ship scheduling model. For each time window, thedelivery location, start date, end date, minimum cargoes (e.g., minimumnumber of deliveries), maximum cargoes (e.g., maximum number ofdeliveries), minimum quantity (e.g., minimum LNG to be delivered withintime span), maximum quantity (e.g., maximum LNG to be delivered withintime span) and/or compatible cargo sizes for each time window. Themaintenance data may include berth and storage maintenance that may behandled by setting berth and storage availability in the LNG shipscheduling model.

The data input may also include optionality opportunities. Theoptionality opportunities may include in-charter, out-charter and/orshort term diversion. The in-charter may include a list of shipsavailable for in-chartering. For the in-charter ships, the data mayinclude ship specifications; daily costs; available time window; type(e.g., one way—paid only for one way voyage and fees, round trip—paidfor roundtrip voyage, and/or long term-paid for long time period withoutspecific voyage info); positioning (e.g., whether or not positioning feeis required and paid); positioning lump sum for fee for positioning;re-positioning, whether or not re-positioning is required and paid;re-positioning lump sum if fee for re-positioning is required; fixedcost (e.g., general fixed costs); and/or daily rate if a daily rate ischarged. The out-charter may include a list of opportunities forout-chartering. The out-charter data may include required shipspecifications; revenue; required time window; ship class (e.g., shipclass required for out-charter); opportunity type (e.g., one way that ispaid only for one way voyage and fees; round trip that is paid forroundtrip voyage; and/or long term that is paid for long time periodwithout specific voyage information); required location (e.g., locationto send ship for out-charter, such as first location in one way orroundtrip); required date, which is the date to send ship; requiredstate, which is the state of ship to arrive at location; completionlocation, which is the location of ship when finished, such as the lastlocation in one way or roundtrip); completion date that is the date theship is finished and at location; end state, which is the state of shipwhen returning; positioning (e.g., whether or not positioning fee isrequired and paid); position lump sum (e.g., fee for positioning);re-positioning, which is a determination whether or not re-positioningis required and paid; re-positioning lump sum, which is the fee forre-positioning; fixed cost, which is the general fixed costs; and/ordaily rate, which is the daily rate charged. The short term diversionmay include required quantity and/or quality; time range; and/or price.

As an example of the optionality opportunity data, the following tabledescribes the comparison of in-charter between incoming voyage,returning voyage, intermediate voyages and number of deliveries, asshown in Table 2. Table 2 summarizes the type and voyage details forin-charters, which is shown below;

TABLE 2 Incoming Returning Intermediate Number of Type Voyage VoyageVoyages Deliveries OneWay FixedCost + 0 DailyRate × 1 PosLumpSum numDaysRoundTrip FixedCost + RePosLumpSum DailyRate × 1 PosLumpSum numDaysLongTerm FixedCost + RePosLumpSum DailyRate × multiple PosLumpSumnumDayswhere FixedCost is the fixed cost, PosLumpSum is the positioning lumpsum amount, RePosLumpSum is the re-positioning lump sum amount, numDaysis the number of days.

Price data is yet another input data type. The price data may includeprice projections for each day or other time period. The price data mayinclude market index, which is the list of market indices for which toassign market pricing; fuel, which is the list of production locationsfor which to assign fuel price; spot, which is the list ofre-gasification terminals for which to assign spot pricing; and/orcontract, which is the list of contracts for which to assign contractpricing. The prices are assumed to be adjusted (e.g., rolling average,or other suitable method) such that price on the day delivered (DES) orpicked up (FOB) is the price expected to be received for revenue androyalty calculations, or cost of fuel on a day for cost calculations.The fuel price may be independent of location and/or region.

Route data is another input data type. The route data may include thenautical distance between production, re-gas, and/or dry-dock locationsbased on various routes (e.g., both ways). The route data may alsoinclude determining whether or not there is a delay due to canal on aroute and/or the cost of transit from canal. The route data may alsoinclude determining whether or not there is a delay due to bunkering ona route, and/or bunker fee. Bunkering is assumed to occur on returnvoyages to production locations.

Complex time windows are yet another input data type. Some complexdeliveries may involve more than single seller, port, and/or buyer. Forsuch situations, the LNG ship scheduling model may include input datathat represents the business needs. For example, the complex timewindows may include various data to model the business needs. This datamay include contract ID1 (e.g., contract for delivery part one);location 1 (e.g., re-gasification location for delivery part one); cargosize 1 (e.g., size of delivery part one); LNG grade1 (e.g., LNG energyquality for delivery part one); earliest start date1 (e.g., earliestdate for complex delivery part one); latest end date 1 (e.g., last datefor complex delivery part one); contract ID2 (e.g., contract fordelivery part two); location 2 (e.g., re-gasification location fordelivery part two); cargo size 2 (e.g., size of delivery part two); LNGgrade2 (e.g., LNG energy quality for delivery part two); earliest startdate 2 (e.g., earliest date for complex delivery part two); latest enddate 2 (e.g., last date for complex delivery part two); minimumdeliveries (e.g., minimum number of complex deliveries, such as splitand/or co-load, in a time window); maximum deliveries (e.g., maximumnumber of complex deliveries, such as split and/or co-load, in timewindow); and/or fiscal relationship. The fiscal relationship may includethe fiscal rules that specify tax rate, profit fraction, royalty ratefor each delivery. Each contract can have complex relationships,correlations across deliveries.

Following table, Table 3, summarizes some of such complex deliveries.

TABLE 3 Number of Number of Number of Delivery Pattern JVs/SellersDischarge Ports SPAs/Buyers Default 1 1 1 Simple split 1 Multiple 1Co-load 1 1 Multiple Co-load with split 1 Multiple Multiple ComplexCo-load Multiple 1 Multiple Complex Co-load with Multiple MultipleMultiple split

In this Table 3, combinations of settings for complex windows. Forexample, one or more JVs and/or one or more contractors may be involvedwith one or more locations.

In addition to the input data, different variables may be utilized bythe LNG ship scheduling model as well. For example, variables can beclassified as two types: decision variables and dependent (auxiliary)variables. Decision variables are the variables that the optimizationalgorithms are manipulating to achieve a desirable objective value.Decision variables specify activities of each ship within the entiretime horizon. For example, the decision variables may include arrivaldate to production or re-gas locations; departure date and/or time fromproduction/re-gas locations; size of assigned cargo size at each tripand associated LNG quality; storage-berth combination which a cargo isserved; travel route; fuel type in each leg of a trip; speed in each legof a trip; departure “state” from regas port (e.g., warm versus cold);whether a ship is in-chartered or out-chartered for a certain period;and/or whether a ship goes to dry-dock on a certain day. The dependent(auxiliary) variables are dependent on the value of the data input anddecision variables. For example, the dependent variables may includestorage levels at production and re-gas terminals at each point in time.

Further, constraints may be used by the LNG ship scheduling model toforce the decisions to comply with some business or physicalrequirements. In the LNG ship scheduling model, constraints shouldsatisfy a feasible solution. As will be explained further below, some ofthese constraints can be considered as soft constraints, which may beable to slightly violate the constraint, while hard constraints are morerigid.

Certain constraints are related to the input data noted above. Forexample, constraints may include compatibility requirements between JVand storage tanks; compatibility requirements between storage tanks andberth to ensure loading a single grade to a ship; compatibilityrequirements between contracts and fuel options and/or third party(e.g., JV) transfers being limited to not exceed a daily maximum amountand maximum net amount. Other constraints may be included in the LNGship scheduling model as specific constraints. For example, constraintsmay include setting arrival and departure timing of vessels to feasibletime periods based on speed, distance, loading/unloading operations;inventory mass balance at production and regasification storage unitsbeing set to a value greater than or equal to zero; inventory ismaintained under storage capacity (e.g., relaxed in soft constraintsextension); loans between JV's have to be balanced by the end of thetime horizon; cargo size has to be less than vessel capacity; berths canbe utilized by only one vessel at a time; total delivery amount and/orquality has to be acceptable based on contractual requirements;deliveries have to be performed within the delivery time windows (e.g.,quantity and time window); deliveries follow ratability windows; and/orvessels have to be maintained within necessary time windows.

The input data and constraints are utilized based on one or moreobjectives. That is, one or more optimization algorithm may be utilizedto manipulate the decision variables to maximize or minimize one ofthese potential objective functions (e.g., the value determination foran objective). The objective functions may include maximizing socialinterest; minimize shipping costs, maximizing joint ventureprofitability, maximizing IOC shareholder profitability and/ormaximizing NOC interest, for example. The maximizing social interest maybe the difference between overall revenue coming from deliveries andshipping costs due to fixed, fuel, canal, and bunker costs. Theminimizing shipping costs may be the shipping cost is based on fixed,fuel, canal, and/or bunker costs. The maximizing joint ventureprofitability (e.g., single JV case) may include maximizing profit of asingle JV, which may apply if there are multiple JV's. The maximize IOCshareholder profitability (e.g., multiple JV case) may includemaximizing profit of an IOC partnering with multiple JV's. Themaximizing NOC interest (e.g., multiple JV case) may include maximizinginterests of an NOC having shares in multiple JV's

With the objectives, one or more algorithms may be used in the LNG shipscheduling model to provide the optimal decision variables. Thealgorithm uses several models derived from the optimality model, whichis a variation of the LNG ship scheduling model. The optimality modelcontains the variables, constraints and objective function which may beone of several alternatives, such as maximization of social economicsand/or minimization of shipping cost. The LNG ship scheduling model andall derived models may be mixed-integer linear models. Details of othermodels are provided further below.

The feasibility model is a relaxation of the optimality model. Itincludes various features, such as objective function which minimizestotal penalty due to infeasibilities. The infeasibilities may includeslacks, such as daily loss production, daily stock-out, violation oflower bound of contractual requirements, and/or violation of lower boundof time windows requirements.

Another model is the in-charter model. The in-charter model is similarto the optimality model except the objective function. This modelincludes features, such as objective function that minimizes cargosdelivered by in-chartered ships. It may include all variables andconstraints of optimality model.

The out-charter model is yet another model. The out-charter model issimilar to the optimality model except the objective function. Thismodel includes features, such as objective function which minimizes thetotal number of cargos that a specific ship delivers. It may include allvariables and constraints of optimality model.

The minimize-ratability-violation (MRV) model is a model similar to thefeasibility model except the objective function and some additionalconstraints. The objective function minimizes the violations due toratability constraints. Model includes an additional constraint to limitthe deterioration in optimal objective value such as social economics.

As noted above, the algorithm may include five stages. FIG. 2B is a flowchart 220 of the five stage algorithm method according to an exemplaryembodiment of the present techniques. In this diagram 220, variousstages may be performed to enhance the LNG ship scheduling model. Instage 1, as shown in block 222, a feasible solution is generated usingfeasibility model. In stage 2, as shown in block 224, an optimalsolution is generated for the feasibility model. The optimal solutionhas no violation and therefore it is also feasible to the optimalitymodel. In stage 3, as shown in block 226, utilization of in-charteredships are minimized using in-charter model. In stage 4, as shown inblock 228, potential out-charter opportunities are searched usingout-charter model. In stage 5, as shown in block 230, an optimalsolution is searched for the optimality model. The output of thesealgothrms is used to perform LNG shipping operations, as shown in block232.

The stages 3, 4, and 5 may follow the described order or may berearranged into a different order to emphasize a different aspect ofoptimal solution. For example, if out-chartering is more beneficial thanlessening in-chartering, then stage 4 may be performed before stage 3.These various stages are further described below.

In stage 1, as noted in block 222, a feasible solution is generated tothe feasibility model. The feasibility model is initialized and thensolved by a rolling time algorithm. The rolling time algorithm breaksthe entire time horizon into a sequence of smaller, but overlapping timeblocks, and solves each smaller problem sequentially. A feasiblesolution is constructed by solving a sequence of optimization subproblems, each of which corresponds to smaller time block. The rollingtime algorithm is shown further in FIG. 3.

FIG. 3 is flow chart 300 of a rolling time algorithm according to anexemplary embodiment of the present techniques. In this flow chart 300,a feasibility model is obtained in block 302. The model variables areinitialized in block 304.

Then, at block 306, the parameters to construct subproblems may bedetermined based on the difficulty of the problem. These parameters mayinclude the total number of time periods that are to be solved in eachsub problem, and the total number of time periods during which theschedule are fixed after a sub problem is solved.

Once the parameters are calculated, subproblems may be constructed andsolved iteratively starting from the beginning of the planning horizonto the end of the planning horizon. For each iteration, a determinationis made whether the current starting time period of the subproblem t isless than T, as shown in block 308. If t exceeds T (e.g., number of timeperiods in the planning horizon, such as 365 days), then ship deliveryschedules have been made for all time periods in the planning horizon,and a feasible solution to the feasibility model is obtained, as shownin block 310.

However, if t is less than T (e.g., number of time periods in theplanning horizon, such as 365 days), the process may be continued byconstructing a subproblem whose objective function coefficients areadjusted in block 312. The adjustment of the objective functioncoefficients may be performed to achieve a reasonable scaling forviolations. For example, the scaling may include penalty rate for cargoviolation of 250; penalty rate for quantity violation of 4; and penaltyrate for volume violation on time t. Then, the penalty rates may beadjusted during the algorithm.

Once the objective function coefficients are adjusted, sub problem maybe created, as shown in block 314. The sub problem is created bypopulating variables, parameters and constraints. Then in block 316, thesolver configuration may be adjusted. The solver adjustment may include,but are not limited to, node limits, time limits and optimalitytolerance. At block 318, the sub problem is solved. The solving the subproblem may include using different stopping criteria based on thedistance between the current time periods and the end of the timehorizon.

Once the sub problem is solved, problem settings may be updated,including the ship schedules for some specific time periods, and thestarting time period of the next potential subproblem, as shown in block320. Then, the update is provided to block 308 to determine whether theprocess should be reiterated or the feasible solution to the feasibilitymodel is determined, as shown in block 310.

In stage 2, the optimal solution to feasibility model is generated. Thismay involve the algorithm performing a sequence of local searches untilthe objective function value of the feasibility model reaches zero. Thesequence of local searches may include two rounds. During the firstround, one-direction search, k-day-flexibility-time-window search,sorted one-ship search and sorted two-ship search are performedsequentially. If the objective function value is positive after thefirst round, it may be the result of the local search getting stuck in alocal optimal solution or infeasibility of the optimality model. In casethe algorithm cannot find a solution with zero infeasibility in thefirst round, a second round is performed. The second round may includeperforming a time-window search, sorted one-ship search and sortedtwo-ship search. If the objective function value is still positive, itis very likely that the optimality problem is infeasible.

As noted above, the local searches here may include a one-directionsearch, a k-day-flexibility-time-window search, a sorted one-shipsearch, a sorted two-ship search, and a time-window search. Theone-direction search may include solving feasibility subproblemsiteratively in which, loading and unloading arcs are fixed to theircurrent values alternatively while all other arcs are left as freevariables. The algorithm is terminated when the objective function valuereaches zero or cannot be improved after a specified number ofiterations (e.g., number of subproblems may be created in thisneighborhood structure). The k-day-flexibility-time-window search mayalso be performed. For this search, each ship is given flexibility toleave several days earlier or later for a given existing schedule. Thenumber of days that each ship is allowed to advance or delay may takevarious values. A typical value used in practice is one day. In thenetwork traveling and waiting arcs for each ship within search timewindow are left as free variables while all other arcs are fixed totheir current value. The algorithm optimizes feasibility model for eachship and the algorithm is terminated when the objective function valuereaches zero or cannot be improved. The sorted one-ship search may alsobe performed. For this search, the schedule for each ship is optimizedwhile schedules for other ships are fixed to a given existing schedule.In the network, the arcs related to one ship are set free while othersare fixed to their current values. The algorithm optimizes feasibilitymodel and is terminated when the objective function value reaches zeroor cannot be improved after a specified number of iterations (e.g., thetotal number of ships). The sequence of ships to optimize depends on thesolution quality associated with each ship. Typically, higher level ofviolation a ship has in its schedule; there is more chance to improveits schedule. Therefore, the ships are sorted according to theirviolation degree in non-increasing order at the beginning of thealgorithm. After each round of local search, the order of ships isupdated based on the new schedule. The sorted two-ship search may alsobe performed. For the sorted two-ship search, schedules for two shipsare optimized simultaneously, while schedules for other ships are fixedto a given existing schedule. In the network, arcs for two ships areleft as free variables while all other arcs are fixed to their currentvalues. The algorithm optimizes feasibility model and is terminated whenthe objective function value reaches zero or cannot be improved after aspecified number of iterations (e.g., the total number of ship pairs).Similar as the one-ship search, the ship pairs are prioritized innon-increasing order based on the total violation. After each round oflocal search, the order of pairs is updated based on the new schedule.The time-window search may also be performed. Time-window search in thisstage is similar to the rolling time window search in stage 1. Thissearch may optimize all ship schedules for shorter time blockssequentially, while fixing the schedule outside of the time block. Theparameters may also be defined to determine the number of periods solvedin a subproblem and the number of time periods where the ship schedulewill be fixed. The algorithm optimizes feasibility model and isterminated when the objective function value reaches zero or cannot beimproved after a specified number of iterations (e.g., the total numberof time windows which can be constructed during one planning horizon).

In stage 3, the in-chartered ships may be reduced. Reducing the usage ofin-chartered ships may have a beneficial impact on the economics. Thisstage of the algorithm seeks potential improvements in the solution bereducing in-chartered ships, while maintaining the delivery plans withinacceptable limits. This stage minimizes the usage of in-chartered shipsone by one, each of which is accomplished in three steps. The first stepinvolves the selection of the next in-chartered ship which is morelikely to be idled compared to other in-charters. In this step, allin-chartered ships may be sorted based on the number of cargos that itcarries. The in-chartered ship that carries the fewest cargos may beselected because it may be easier to idle such a ship by switching fewercargos to other ships. The second step tries to use sorted two-shipslocal searches to totally idle the selected in-chartered ship byshifting/swapping cargos. Similar to the operation in stage 2, schedulesfor two ships, the selected in-charter and another ship that may takeover cargos from the selected in-chartered ship are optimizedsimultaneously, while maintaining other ships fixed. The only differencefrom the step in stage 2 and this step is that the in-charter model isused where the objective is to minimize the number of cargos that theselected in-chartered ship carries. The algorithm optimizes in-chartermodel and is terminated when the objective function value reaches zero,is below a threshold or after a specified number of iterations (e.g.,the total number of ships that may take over cargos from the selectedin-chartered ship). Then, the time window search is performed in thethird step. Similar to the step in stage 2, this search optimized shipschedules for shorter time blocks sequentially, while fixing theschedule outside of the time block. There are two differences betweenthe step in stage 2 and this step. One difference is that, the timewindows is constructed around the period when the in-chartered ship hascargo delivery; and the other is that in-charter model is used with theobjective to minimize the number of cargos for the selected in-charteredship.

In stage 4, out-charter opportunities are pursued. Out-chartering a shiphas a beneficial impact on the economics as long as it does not affectthe delivery plans. Given a feasible schedule for the optimalityproblem, this stage tries to maximize the number of ships to beout-chartered. This stage of the algorithm investigates out-charteringeligible ships one by one by using three steps. In the first step, allships, which are eligible for out-charter opportunity may be sortedbased on the number of cargos. The ship carrying fewest cargos may beselected since it may be easier to switch fewer cargos to other ships.Then, in the second step, local searches are used to totally idle theselected ship by shifting/swapping cargos. These searches may includethe sorted two-ship search and the time-window search similar to thesteps in stage 3. Instead of the in-charter model used in stage 3,out-charter model is used to minimize the total number of carryingcargos for the selected ship. In the third step, the assignments arefinalized between the idled ships and out-charter opportunities. Notethat this part of the algorithm is specifically designed for pursuingout-charter opportunities that require a ship to be idle for the entiretime horizon. Shorter term opportunities can be materialized in stage 5.

In stage 5, optimal solution is determined for the optimality model. Thealgorithm may perform a sequence of local searches until the objectivefunction value of the optimality model is within a threshold, or doesnot improve for a specified number of iterations. The local searchesinclude time-windows search, one-ship, and two-ship search, which arediscussed below.

The time-windows search may be similar to the rolling time algorithm instage 1. This search may optimize all ship schedules for shorter timeblocks sequentially, while fixing the schedule outside of the timeblock. The parameters may include number of time periods solved in thesub problem, and number of time periods fixed after the sub problem issolved.

Further, the one-ship search and the two-ship searches may be performedsimilar to the algorithm in stage 2, which is noted above, with theexception that this local search is applied to the optimality problem.

With regard to the soft constraint extensions to LNG ship schedulingmodel, a feasible solution for the LNG ship scheduling model (e.g.,optimality model) satisfies all the constraints. The benefit ofsatisfying feasibility is not the same for each of the constraints.While some of the constraints need to be satisfied due to thecontractual or physical capacity restrictions (e.g. inventory and/orproduction), others, such as ratability constraints, present users' orcustomers' preferences and can be slightly violated. Such softconstraints may enhance the solution algorithm. That is, softconstraints may be applied either because it is difficult to find afeasible solution when hard constraints are used (e.g., infeasible ortime consuming) or because economics improvement is restricted due toburdensome hard constraints. Accordingly, soft ratability constraintsare designed to facilitate limited flexibility for feasibility andeconomics; while soft inventory levels are designed for feasibility.

As an example, the soft constraints may be utilized to enhance thesolution algorithm. In particular, the soft constraints may enhancesolutions for situations where it is difficult and/or impossible to finda feasible solution due to hard constraints. These situations mayinvolve those having infeasible solutions and/or very few feasiblesolutions (e.g., performance of algorithm may not complete within anacceptable time period). Other situations may involve those where theeconomics deteriorate drastically due to restrictions by hardconstraints. Specifically, relaxing some of strict ratabilityconstraints may allow some deliveries to be made a day early or late.This may not cause any problem for the LNG operations and may createadditional value.

To provide advantages for this flexibility to enhance the algorithm'sperformance, the LNG ship scheduling model may include soft constraints,such as soft ratability constraints and/or soft inventory constraints.The soft ratability constraints may include ratability constraints thatrepresent users' or customers' preferences. These may guide the LNG shipscheduling model to make certain deliveries in a desired fashion, butcan be slightly violated (e.g., adjusted to have a larger tolerancerange) without creating any problems for the LNG shipping operations.The soft inventory constraints may include shipping and productiondisruptions that lead to changes in projected inventory levels.Therefore, some of the physical inventory restrictions may not have tobe enforced strictly for the entire year. Imposing such hard inventoryconstraints for the entire planning horizon may unduly restrict thesolution space. As shown in block 242, the algorithm may be automated toidentify constraints that can be modeled as soft constraints.

With regard to soft ratability constraints, each customer has a specificpreference for deliveries throughout the year. For example, the customermay prefer the deliveries to be evenly distributed throughout the year.Optimality model includes constraints for ratability windows for eachcontract, which enforce a proportional shipment to a certainlocation/contract throughout the year.

Various mechanisms may be utilized to implement ratability windows inthe LNG ship scheduling model. As an example, the ratability constraintsmay include monthly; bi-monthly, bi-monthly with one month overlap;quarterly; quarterly with one month overlap; quarterly with two monthoverlap. Further, the upper and lower bound quantity for each ratabilitywindow can be generated in various manners. As an example, two differentlogics, based on the variety of vessels serving a specificcontract/location, and one logic based on aggregation is describedbelow. If the size of ships serving a location is close to each other,then the following logic for each contract may be implemented:

TargetQuantity(tw)=TargetMMBTU/NofRatability Windows

avgTargetNOfDeliveries(tw)=TargetQuantity(tw)/(AvgShipSize*EnergyContent)minTargetNumberOfDeliveries(tw)=max(0,floor(avgTargetNOfDeliveries(tw))−1)maxTargetNumberOfDeliveries(tw)=ceiling(avgTargetNOfDeliveries(tw))minQuantity(tw)=minTargetNumberOfDeliveries(tw)*minshipsize(c)*EnergyContentmaxQuantity(tw)=maxTargetNumberOfDeliveries(tw)*maxshipsize(c)*EnergyContentwhere TargetMMBTU is the target annual delivery quantity fromcontractual obligations; minshipsize, maxshipsize, AvgShipSize: min,max, average size of ships which can serve to a specific contract;minQuantity, maxQuantity, TargetQuantity: min, max, target quantity ofLNG to be delivered in a time window; minTargetNumberOfDeliveries,maxTargetNumberOfDeliveries; avgTargetNOfDeliveries: min, max, averagenumber of deliveries for a ratability window; EnergyContent: energycontent of LNG; TargetMMBTU: target amount of LNG to deliver in MMBTUunits; and NofRatabilityWindows: number of ratability windows in oneyear. The logic uses the similar ships are similar than it is enough toprovide one to two delivery flexibility for each time window.

If the size of ships serving a location are different from each other(above a threshold), then the following logic may be implemented:

TargetQuantity(tw)=TargetMMBTU/NofRatability Windows

minQuantity(tw)=max(0, TargetQuantity(tw)−downflex)maxQuantity(tw)=TargetQuantity(tw)+upflex;, where downflex and upflex are defined as:Downflex=maxshipsize*EnergyContentUpflex=maxshipsize*EnergyContent

Another logic is to aggregate (e.g., sum) the monthly upper and lowerbounds of two months and generate upper and lower bounds for bi-monthly;and similarly summing bounds of three months for calculating quarterlyratability windows. Using aggregate ratability windows creates arelaxation of the original model.

In other embodiments, it may be preferred to have deliveries to be maderoughly even without satisfying the ratability constraints exactly.Accordingly, this type of business constraints may be represented usingsoft constraints. While multiple methods may be utilized to incorporatethe soft ratability constraints into the current algorithm, twoalternative approaches are described below, which include the minimizeratability after stage 5 and minimize ratability after Stage 2.

In the proposed algorithms, two or more sets of ratability restrictionsmay be used (e.g., original and looser (or aggregate)). The originalconstraints are typically created based on business needs. For instanceif a customer requests deliveries to be roughly equal in each month,then similar lower and upper bounds on deliveries are provided for eachmonth. The proposed algorithm may be automated to decide which type ofaggregation to use based on model inputs such as the number of vessels,number of contracts, number of deliveries, and original ratabilityrequirements.

With regard to the minimize ratability violation after stage 5 method,the algorithm first decides on the aggregate ratability constraintsbased on model inputs. Stages 1 to 5 is applied with ratabilityconstraints which are looser (or aggregate) than original model. Forinstance, if the original model has monthly ratability windows, thenaggregate model may have quarterly ratability windows. Aggregate(Quarterly) may be generated in several potential approaches providedabove. The objective of the aggregate ratability is to lessen solutiontime without compromising the quality of optimal solution. At the end ofstage 5, algorithm finds a feasible solution to the model with aggregateratability windows. This solution may or may not be feasible withrespect to the original ratability (e.g., monthly). Then, in a MRV stage(Minimize Ratability Violation), the algorithm minimizes ratabilityviolations with respect to the original ratability windows. The modelmay also include an additional constraint, which limits deterioration inthe objective function value to a range within some tolerance around theoptimal solution found in stage 5. The MRV stage, similar to stage 2,may be solved using one-direction search, time-window search, sortedone-ship search, and sorted two-ship search.

As an example, the method may include the following stages. In stage 1,the feasible solution to feasibility model is generated using aggregateratability windows. Then, in stage 2, the optimal solution tofeasibility model is generated using aggregate ratability windows. Atstage 3, the in-charter usage is reduced using the aggregate ratabilitywindows. Then, at stage 4, the out-charter opportunities are pursuedusing the aggregate ratability windows. At stage 5, optimal solution tooptimality model is calculated using the aggregate ratability windows.Then, following stage 5, ratability violation with respect to originalratability windows are calculated. Following this calculation, theminimize ratability violation using the original ratability windows iscalculated in the MRV stage.

If aggregate ratability windows are based on quarters, and originalratability windows are based on months. Then, stages 1 to 5 optimizeusing quarterly ratability windows and produce an optimal solution basedon quarterly ratability windows. The quarterly ratability solution isthen evaluated based on monthly ratability windows. If there is anyviolation, the MRV stage minimizes the resulting violations, whilesustaining the economics within a certain level same as or close to themaximized economics achieved in stage 5. With regard to the minimizeratability violation after stage 2 method, this method is similar to themethod above, but the order is adjusted. First algorithm determines theaggregate ratability constraints based on model inputs. Stages 1 and 2use the aggregate ratability windows, and generate a feasible solution.Stage MRV takes the feasible solution, which is based on aggregateratability, and minimizes violations with respect to the originalratability windows. The stage MRV may or may not eliminate each of theratability violations. After the stage MRV, stages 3 to 5 follow tofurther improve the solution.

As an example, the method may include the following stages. In stage 1,feasible solution to feasibility model is generated using the aggregateratability windows. In stage 2, the optimal solution to feasibilitymodel is generated using the aggregate ratability windows. Then, theratability violations with respect to original ratability windows arecalculated. Following this calculation, the minimal ratability violationis determined in the original ratability windows. Then, in stage 3,in-charter usage is reduced using the original ratability windows. Instage 4, out-charter opportunities are pursued using the originalratability windows. Then, in stage 5, the optimal solution is determinedfrom the optimality model using the original ratability windows.

If the aggregate ratability windows are based on quarters, and originalwindows are based on months, the stages 1 and 2 use quarterly ratabilitywindows. At the end of stage 2, the algorithm may have a feasiblesolution based on quarterly ratability windows. This solution is thenevaluated based on monthly ratability windows and if there is anyviolation, the MRV stage minimizes the resulting violations. Then,stages 3 to 5 are performed to enhance the solution, while sustainingthe violation towards the monthly ratability within a pre-specifiedtolerance.

These two approaches are two exemplary algorithms that may be used toaddress the soft ratability issue. The minimize ratability after stage 5method is directed to providing the best solution through stages 1 to 5and then minimize violations as much as possible. In the minimizeratability after stage 2 method, the method is directed to feasibilitybecause the ratability violations are minimized after stage 2. Thisapproach may help for situations where it is difficult to get a feasiblesolution. After ratability violations are minimized, stages 3 to 5 areperformed to enhance the solution.

As an example, FIG. 2C is a flow chart 240 of the five stage algorithmwith soft extensions method according to an exemplary embodiment of thepresent techniques. The reference characters from FIG. 2B are includedmay be referenced as described above. In addition to the five stages,the method includes a violation calculation with respect to originalconstraint (e.g., window or range around the constraint), as shown inblock 248. The violation may be a ratability violation and the originalconstraint may be an original ratability window or other soft constraintwindow or range. Further, as shown in block 250, the minimal violationis calculated. The violation may be a ratability violation (MRV) forcertain embodiments.

With regard to soft inventory constraints, LNG ship scheduling modelsmay be optimized during the preparations for next year's delivery plan.Once the ADP is finalized and operations start, shipping and productiondisruptions may lead to changes in projected inventory levels. Theinitial plan may involve regular adjustments to account for thesevariations. Imposing such hard inventory constraints for the entireplanning horizon may restrict the solution space more than it is needed.Therefore some of the physical inventory restrictions may not be treatedas soft constraints.

Similar to the example for soft ratability constraints, the algorithmcan be extended to address soft inventory issues. As shown in block 244,the proposed algorithm may be automated to decide how to relax theinventory constraints based on model inputs such as the number ofvessels, number of contracts, number of deliveries. A relaxed set ofinventory constraints can be used to find a feasible solution and thenthe violations can be minimized in a manner similar to the method usedin the stage MRV, as described for the ratability example. In stages 1and 2, the feasibility model may be used to reduce the penalty withrespect to the relaxed constraints to zero. The solution at the end ofstage 2 may still have violations in inventory constraints as evaluatedwhen using the original (e.g., tighter) bounds. During stages 3 to 5,the algorithm seeks to constrain the violation in inventory constraintsas evaluated for the tighter bounds such that the violations do notexceed the violation obtained at the end of stage 2. The penaltystructure for violating the inventory constraints may be set in variousmanners. There may be multiple techniques of creating relaxed inventoryconstraints. One example is to increase inventory capacity over timesuch that relaxed capacity is always higher than the original inventorylevels. The penalties can also be differentiated by storage units.Similarly, the relaxed bounds for inventory constraints can be generatedin various manners. For example, the relaxed bounds may be based ondegree of flexibility in re-routing production to other storagefacilities, or on degree of flexibility in securing additional storagefrom another entity that shares storage at that facility.

With the input data, constraints and objectives, the LNG ship schedulingmodel may be used to predict decision variables. The model, which is amathematical model, is further described in Appendix A as an ADP model.

LNG Ship Re-scheduling Optimization System

With regard to -LNG ship re-scheduling (e.g., 90-day plan), theoptimization system may develop updated LNG loading and deliveryschedules based on new planning information, and/or in response to adisruption event or customer request or new market opportunity.

The -updated ship schedule may be provided by a re-schedulingoptimization system by performing analysis of the input data through theuse of one or more objectives, constraints and algorithms, which may besimilar to the discussion above for the ship scheduling model. However,the re-scheduling system is intended to be used more frequentlyconcurrently with the updates of LNG shipping operations, disruptions,customer requests and/or new market opportunities. This system can beused for 90-day plan development, ship schedule repair and ADPnegotiation. Further, the algorithm may include different stages, suchas (i) stage A that recovers feasibility; (ii) stage B that minimizeschanges; (iii) stage C that maximizes economics, while sustainingminimum changes; (iv) stage D that-maximizes economics, which may inducesignificant number of changes to the baseline schedule; and/or (v) stageE that minimizes changes while sustaining the maximum economics. Anexample of one embodiment of the system for developing 90-day plan isshown in FIG. 4.

FIG. 4 is a block diagram 400 of a LNG ship re-scheduling optimizationmodel according to an exemplary embodiment of the present techniques. Inthis diagram 400, the input data 404 (e.g., baseline schedule (which maybe the ADP or another baseline schedule), updated planning assumptions,user preferences, such as the degree of flexibility for various changes,historical actuals data (e.g., deliveries, inventory and the like)) isprovided to an optimization system 402 to create an updated shipschedule 406. The optimization system 402 includes a user interface 408,intelligent model preprocessor 418, optimization models 410 andoptimization solution method 412, which each communicate with eachother. The updated ship schedule 406 is communicated to the user system414, which is provided to the customer system 416.

This system extracts different information from different scenesaccordingly (e.g., new planning information, disruption, new customerrequest and new opportunity) and use them in an intelligent manner toupdate delivery schedules in a more efficient manner. The systemincludes a suite of optimization models 410 and specifically designedalgorithms (e.g., optimization solution method), which recover theschedule from disruptions (e.g., resolve the infeasibility caused by theupdated information), minimize changes with respect to a baselineschedule, maximize economics within some specified tolerance for changesto schedule, evaluate new market opportunities, and minimize changesagain while sustaining the maximized economics which are achieved afterconsidering the new market opportunities.

The system can be used to develop 90-day delivery schedules (e.g.,90-day plans). The system can also be used to recover delivery schedulefrom disruptions (e.g., ship breakdown, terminal breakdown, etc.). Thesystem can also update the delivery schedules beyond the 90-day periodto enable analysis around the effects of disruptions and changes made inthe 90-day period on planned deliveries further out in the future. Thesystem can also be used to investigate different market opportunitieswithin 90-days or further out in the future. The system can also be usedto experiment the tradeoff between maintaining schedule stability andincreasing customer satisfaction or pursuing new market opportunities.The system can also be used to prioritize changes that are applied torecover schedules. The system can also be used in negotiating annualdelivery programs. In developing the updated delivery schedules, thesystem can optimize ship schedules, terminal inventory management, LNGproduction schedules, maintenance schedules, and other shippingdecisions, while accounting for tradeoffs related to various options inavailable shipping, customer requirements, price uncertainty, contractflexibility, market conditions, and the like. Other shipping decisionsmay include ship fuel selection, ship speed, and/or heel selection forevery voyage.

FIG. 5A is a block diagram 500 of how the system may be implemented forupdating ship schedules according to an exemplary embodiment of thepresent techniques. This system includes the input data 502, theintelligent model preprocessor 504, the model and algorithm system 506,and the output data 508 with the illustration of how they could worktogether to realize the objective of the present techniques. The inputdata includes two categories: data in one category is the originalinformation used to generate the baseline schedule (e.g. annual deliveryplan, another baseline plan, etc.) as discussed above, and data in theother category represents the new and/or updated information and the newobjective requirements. The intelligent model preprocessor 504translates the updated information and business preferences in anintelligent manner that the model and algorithm can use the translatedinformation to recover schedules from disruptions or evaluate new marketopportunities in an efficient manner. Then, the system creates atime-space network model (e.g., LNG ship rescheduling model) and appliesa suite of specially designed algorithms to solve it based on thespecifications of the objectives. The outputs of the system may be usednot only for ship scheduling, but also for inventory management,optionality utilization, and/or maintenance schedules.

A portion of the LNG ship re-scheduling optimization model may utilizeone or more of the input data associated with the long-term planningmodules to provide consistency between the long-term production plan andcontractual obligations. The input data 502 may be categorized as:production data at one or more liquefaction terminals, facilitymanagement data at production terminals, customer requests, customerterminal data, market conditions, contracts and/or shipping data.Production data may include data relating one or more of: the types orgrades of LNG produced and their heat content; production rates of oneor more types or grades of LNG; production unit maintenance schedulesand associated flexibility in scheduling the maintenance. Facilitymanagement data may include data relating one or more of: the number ofberths available for loading; storage capacity for each type or grade ofLNG; connection between berth and storage unit; and berth maintenanceschedules and their associated flexibility. Customer requests mayinclude input data relating to one or more of the ratability ofdeliveries; the time window and cargo sizes for specific deliveries; thespeeds that the ships should take; and the fuel modes that the shipsshould use. Customer terminal data may include input data relating toone or more of: storage capacities for each type or grade of LNG; thenumber of berths available for unloading; regasification rate schedules;and distances from each liquefaction terminal. Market conditions mayinclude input data relating to one or more of: the outlook for indexprices to be used in pricing formula; the outlook for future marketopportunities such as spot sales; and futures and forward contractprices. Contracts may include input data relating to one or more of:terminals where LNG can be delivered; annual delivery targets for eachcustomer terminal; ratability of delivery, which is the timing andspacing of delivery of portions of an agreed-upon amount of LNG; gasquality, type, or grade to be delivered; pricing formulas; diversionflexibility; and other types of flexibility such as downflex (an optionwhereby the buyer may request a decreased quantity of LNG). Contractsmay also include input data relating to the length of contract to whichone or more LNG customers are bound. For example, an LNG customer may bebound by a long-term contract, such as sales and purchase agreements ora production sharing contracts. Shipping data may include input datarelating to one or more of: a list of leased DES (delivered ex ship),CIF (cost, insurance and freight), CFR (cost and freight) and availablespot ships; a list of FOB (freight on board) ships for each customer,the ships typically being owned or leased by the customer; shipcapacities; restrictions on what ship can load/unload at what terminal;maintenance schedules for ships; cost structures for ships; boil-off andheel calculations for each ship, including an optimal heel amount upondischarge at a regasification terminal; range of ship speeds andassociated cost profile; and/or set of allowed fuels.

Further, the input data into the LNG ship rescheduling model may includeother data, such as time horizon (e.g., start time, end time, and/ortime discretization); ship initial information (e.g., start time, startlocation, start heel status, LNG volume on board, grade for LNG onboard, contract for LNG on board, needs to redirect to anotherdestination location); updated planning information towards ships (e.g.,daily availability, fuel modes, speed levels, heel options, and/orloading sizes); updated production/regasification profile (e.g., dailyproduction rates and/or daily regasification rates); updated inventoryprofile (e.g., initial inventory level at each storage tank and/orinventory capacity for each JV); updated maintenance schedule; newin-chartering offers; new out-chartering opportunities; new diversionopportunities; new spot market opportunities; new backhaul options;baseline schedule including a list of cargos (e.g., delivery contract,delivery location, delivery ship, delivery time, and/or deliveryquantity); change flexibility for each cargo (e.g., cargodivertable/optional, ship change allowed, time change allowed, number ofdays that it can advance or delay, and/or quantity change allowed oramount of quantity that can be reduced or increased); relativeimportance of each change; objectives: minimize changes; maximizeeconomics; minimize cost, etc.; and/or thresholds towards changes,economics, cost etc.

In addition to the input data, the LNG ship rescheduling model mayinclude constraints. Appropriate constraints and specifications arelayered onto the LNG ship rescheduling model for conducting variousanalyses that are specific to problem being addressed. The constraintsin the LNG ship rescheduling model may be similar to those in the LNGship scheduling model, which is noted above. Further, the coreoptimization model provides the ability to model a supply chain thatincludes one or more LNG liquefaction terminals, multiple customers withlong and short-term purchase contracts, and spot LNG markets/buyers. Thesupply chain also has the capability to include multiple fleets ofheterogeneous time-chartered ships together with opportunities toin-charter or out-charter ships. When dry-docks are required, the modelalso ensures the ships to take dry-docks within specified time windows.The model also provides functionality to model contractual flexibilitiesand market opportunities such as opportunities to divert cargo orbackhaul third party cargo on a return voyage. Detailed operationalconstraints related to liquefaction terminals and shipping, contractualobligations and market conditions are included in the model.

The input for contracts may consider the possibility of multipleoperating entities or owners involved in a liquefaction project, andeach operating entity or owner may have a joint-venture agreement orother arrangement with the natural gas producer (e.g., national oilcompany or government (NOC)) including fiscal agreements that coverroyalties and taxes. Another type of contract covered by contractsarises in liquefaction projects with shared facilities andinfrastructure (such as storage facilities, loading facilities, andships), in which there exist facility sharing agreements and fleetsharing agreements between the different production entities (e.g.,joint ventures (JVs)). These agreements may include terms outliningvolume storage entitlements, lifting entitlements and cost-sharing termsamong others. Further, sales and deliveries typically are negotiatedbetween a production entity and a buying entity, such as a joint ventureand a customer. These contracts can be long-term or short-term andinclude SPAs (Gas Sales and Purchase Agreements) and PSCs (ProductionSharing Contracts). These agreements typically include terms related toannual volumes, ratable delivery requirements, ships required fortransport, price and penalties. In any of these multiple entity—typecontracts or agreements, it is possible that various parties areoperating under different fiscal conditions. For example, two companiesmay be working with a national oil company on a liquefaction projectunder different joint venture agreements. For various reasons thosejoint venture agreements may have different terms governing the abovetypes of subjects. These different terms are factored into the shipscheduling optimization model as disclosed herein.

The system takes as input a baseline schedule, customer preferencesregarding flexibility in different kinds of changes to the schedule,updated information regarding assumptions that are used to build theschedule, and new market opportunities. The system develops an updatedschedule that minimizes the degree of change with respect to thebaseline schedule. The system also generates the most economic schedulethat does not deviate from the baseline schedule by more than apre-specified tolerance for changes. The system also investigates newmarket opportunities while minimizing impact on the baseline schedule.

In the baseline schedule, a list of planned cargos is specified with thefollowing aspects for each cargo: the customer, the destination, thedelivery ship, the delivery time and the delivery quantity. All of theseaspects can (sometime have to) be changed in the updated plan. Forexample, if a ship is delayed 1 day, the delivery time for some cargosmay have to be delayed 1 day. As another example, if a ship is brokendown, an extra ship may have to be in-chartered to cover the cargoswhich are planned for the broken ship. If the in-charted ship hasdifferent capacity, the delivery quantity is also changed. As anotherexample, if a destination terminal is shut down, the cargos which areoriginally planned to be sent to this terminal have to be diverted toanother destination. As another example, the customer may request tosend the cargo to another destination with a bigger ship on a differentdate, and therefore multiple aspects of the cargo are changed.

The system provides the user the ability to specify the allowedflexibility in changing different aspects of the schedule. For example,the user may specify whether the ship associated with a planned cargocan be changed in the updated plan. As another example, the user mayspecify the amount of time by which the delivery date for a cargo can bedelayed (or advanced) in the updated schedule. As another example, theuser may specify the amount by which the quality of LNG to be loaded ordelivered on a planned cargo could be changed in the updated schedule.As another example, the user may specify whether or not a planned cargoneeds to be delivered to the planned customer/location at all. Asanother example, the system provides the user the ability to specifywhether or not extra ships could be in-chartered or out chartered in theupdated schedule.

The system provides flexibility for the user to specify the relativeimportance of the different types of changes mentioned above, whileminimizing the overall differences between the baseline schedule and theupdated schedule. For example, the user may specify a higher priorityfor minimizing changes for deliveries to a specific customer, or forminimizing changes to deliveries in the specific period of the calendar.As another example, the user may prefer to make a sequence of ship swapsor date changes than to rent extra in-charters. As yet another example,the user may prefer ship changes instead of delivery date changes, orvice versa.

As specified above, the system may also obtain as input the new and/orupdated information regarding assumptions that pertain to the forwardplanning period. These assumptions can be different from those that wereused to build the baseline schedule. The system may provide the user theability to provide updated information or consider more decision optionswhich can only be practical during short-term periods. For example, theuser can specify the initial status for ships at the beginning of theplanning period with different granularities including: the earliestavailable date, location, heel status, volume on board, LNG quality onboard and contract information. As another example, the user can specifyconstraints on the daily availability for ships. Some of thisinformation may not have been available and/or accurate when thebaseline schedule was initially developed. As yet another example, theuser can update the initial inventory level in terminal storage at thebeginning of the planning period which can be different from what waspredicted in the baseline schedule. In another example, the user canupdate the projected daily production/regasification rates. Also, theuser can specify short-term out-chartering opportunities which were notforeseen when the baseline schedule was developed. For example, the usermay want to move the planned dry-docking events from one period toanother period in order to ensure ample shipping resource during theperiods when the LNG market price is higher according to the updatedpricing information. Further, the user may want to consider different ormultiple fuel or speed options in the short-term periods instead of theoptions used in baseline schedule. As another example, the user may wantto adopt different loading strategy including partial loading, split andco-loading instead of the strategy used to make a long-term plan, etc.

Some of these information updates, especially those related to changesfor ships, facilities, production and/or deliveries, often causesinfeasibility of the baseline schedule (e.g., disruptions). As example,if a ship is delayed, it may not be able to complete all deliveries ontime as planned in the baseline schedule. For example, if a ship isbroken down, other ships may have to change their schedules or extraships may have to be in-chartered in order to cover the deliveries forthe broken ship. As another example, if a destination terminal is shutdown, all cargos which are planned for this terminal may have to bediverted to other locations. As yet another example, if initialinventory level at a production terminal is much higher than what ispredicted in the baseline schedule, some cargos may have to be enlargedor extra cargos may have to be added in order to consume the extravolume. For example, if the daily production rate is higher than what isassumed in the baseline schedule, cargos may have to be loaded fasterand extra cargos may have to be added to avoid lost production. Asanother example, if a customer requests to change the delivery time ofhis cargos, the entire schedule may have to be changed because shipavailability may be changed and loading/unloading schedules may also bechanged. As yet another example, if the user wants to move a dry-dockevent from one period to another period, all schedules in between thesetwo periods may be affected and need to be re-planned.

Disruption types that user may face in LNG supply chain optimization arepre-built in the intelligent model preprocessor. When new and/or updatedinformation is provided, the preprocessor may first determine whether ornot and what disruptions may be caused. If disruptions are caused, thepreprocessor may translate the information through a pre-defined schemeinto objectives and constraints into the model to recover the schedulefrom disruptions in a more efficient manner before the algorithms puteffort to minimize deviations from baseline schedule.

Other information updates, together with new market opportunities, maynot affect feasibility. That is, the baseline schedule may stillfunction if the new options or opportunities are not pursued. Byconsidering the new options or opportunities, however, economicefficiency may be improved with potentially significant deviation fromthe baseline schedule. For example, the user can investigate the benefitto slow down the ship fleet by allowing a slower speed than that used inthe baseline schedule. For example, the user can improve the netback byallowing some specific fuel modes other than the standard mode used inthe baseline schedule. As another example, the user can reduce shippingcost and avoid ship spare time by out-chartering additional ships whilenot affecting the reliability for the transportation networksignificantly. As yet another example, the user can generate higherprofit by diverting cargos to spot markets with high price.

Similar as disruption types, opportunity types that user may consider inLNG supply chain optimization are also pre-built in the intelligentmodel preprocessor. Information related to opportunities may also betranslated through a pre-defined scheme into objectives and constraintsin the model to provide insight in evaluating the economic benefit in anefficient manner. If taking the opportunity may cause significantdeviation from the baseline schedule, however, the opportunity may notbe considered when the system focuses on schedule recovery and deviationminimization; instead, it may be investigated separately followed by thedeviation minimization again.

The system may also take other inputs related to setting up theobjective function including user preferences on flexibility to variouskinds of changes for each cargo and user preferences on the relativeimportance of different kinds of changes. Inputs could also include userpreferences for the objective function to be optimized. The selectionsmay include minimizing changes compared to a baseline schedule,maximizing economics, minimizing changes for a given economicperformance, or maximizing economics for a given level of changes.Economic maximization may include user-defined model objectives such asmaximizing LNG throughput, minimizing costs, maximum profits, maximizinggross netback, optimizing optionality, and/or maximizing robustness inface of uncertainty related to weather conditions, travel times, ormarket conditions.

The models for LNG ship rescheduling optimization also have time-spacenetwork structure similar as in the long-term ship schedulingoptimization with specific modifications. These modifications areassociated with the time horizon, cargo expression and/or shipavailability.

For example, the time horizon may include the length of planninghorizon, which is adjustable. That is, the time horizon may be anypractical number of time periods, which is not limited to 90-dayperiods. Also, besides the planning horizon, a posterior horizon mayalso be created in the network, which includes an appropriate number oftime periods further out in the future. The posterior horizon providesthe model ability to incorporate the future and make better decisions byavoiding unnecessary costly changes or increasing chance to take marketopportunities during the planning horizon. For an example, during theplanning horizon, daily production rates are higher than what werepredicted in the baseline schedule and one spot cargo needs to bedelivered by the end of the horizon. An extra ship may have to bein-chartered for this spot cargo if the model cannot see that a ship(not in-chartered) may idle for a long time period since the end of thisplanning horizon. For example, in the baseline schedule, there is adivertible cargo whose delivery time is close to the end of the planninghorizon. However, the spot market that may take this cargo is muchfarther away than the cargo's original destination. The cargo can bepossibly diverted only if the network includes the posterior periodsbecause, otherwise, no decision variables associated with this diversionmay be created in the network. Moreover, degree of flexibility (e.g.,number of decision variables) in different horizons may be adjustable.For example, all operational options (e.g., fuel modes, speed levels,heels etc.) may be considered in the planning horizon to make apractical delivery schedule in near future. However, only the standardoptions, which are used in the long-term planning, may be considered inthe posterior periods since this horizon is further out in the futureand the corresponding planning information is less accurate and moreuncertain.

As another example, these modifications may include cargo expression.That is, instead of the general exclusive time windows expressed in thenetwork for the long-term planning, individual cargos are expressedexplicitly in the network in this problem. By including cargos in thearcs, all changes associated with cargos can be tracked precisely, whileonly the binary decision variables towards arcs need to be created.

As yet another example, the modifications may include ship availability.When a ship is unavailable on a specific time period, all traveling arcs(e.g., ship is always on duty on traveling arcs in the network), whichincludes that time period may not be created in the network to: 1)reduce the network size and speed up the solution searching; and 2)avoid using constraints to handle the ships' unavailability and soreduce the difficulty of the model.

For the LNG ship rescheduling model, the variables may include differenttypes of variables associated with different aspects. For example,variables in the model may include cargo variables; contract variables;spot market opportunity variables; when to dry-dock each ship thatrequires dry-docking variables; what ship to out-charter and whenvariables; and/or what ships to in-charter and when variables.

The cargo variables may include whether it may be diverted; load timing,discharge timing; load quantity, discharge quantity; assigned ship,destination terminal; heel left on ship; ship speed and fuel selectionfor laden voyage; ship speed and fuel selection for ballast voyage; whattank farm to load cargo at; and/or what berth to load cargo at. Thecontract variables may include how much quantity to divert; which cargoto divert; how much extra spot sale to take; and/or how many extra spotcargos to take and when. The spot market opportunity variables mayinclude whether it may be taken; how much quantity to deliver, and/orhow many cargos to deliver and when.

In addition to the variables, the LNG ship rescheduling model may alsoinclude various constraints. The time space network model for thelong-term planning problem can be applied for this problem includingadditional constraints related to the cargos with their specificflexibilities towards: delivery contract, delivery destination, deliveryship, delivery time, and/or delivery quantity. The delivery contractand/or destination constraints may include a determination whether acargo is not divertible/optional (e.g., the cargo is enforced; the cargocan only be delivered to its original contract and destination; thecargo has to satisfy the other restrictions (delivery ship, time,quantity etc.)) and/or a determination whether the cargo isdivertible/optional (e.g., diversion is favored, for example,contract/destination change may not penalized; and/or the otherrestrictions (delivery ship, time, quantity etc.) are deactivated). Thedelivery ship constraints may include a determination that the cargo canonly delivered by the original ship if ship reassignment is not allowed;a determination that the cargo can also be delivered by the othercompatible ships if ship reassignment is allowed; and/or a determinationthat the delivery ship changes may be penalized. The delivery timeconstraints may include a determination that the cargo cannot bedelivered beyond the specified time window; a determination that thecargo can be delivered at any time period within the specified timewindow; and/or a determination that the delivery time changes may bepenalized. The delivery quantity constraints may include a determinationthat the cargo cannot deliver LNG less than or more than the specifiedrange; a determination that the cargo can deliver any LNG quantitywithin the specified range; and/or a determination that deliveryquantity changes may not be penalized.

With regards to the intelligent model preprocessor, this processor is atranslation layer between the input data and the optimization model,which may translate the practical information provided in the input datato the advanced information used by the model and algorithms. Thispreprocessor may provide a mechanism to translate business preferencesand new/updated information into constraints and objectives that can bebuilt into the model, and guide the optimization solver searchingsolutions towards feasibility in a more efficient manner whendisruptions are faced or towards optimality in a more efficient mannerwhen opportunities are investigated.

This preprocessor may also include a series of criteria which reflectmanagement options or preferences, a scheme to manipulate cargos whichmay be changed due to the updated information, and a penalty calculatorwhich assigns cargo specific penalty weights for changes in theschedule. When updated information is provided in the input data, thispreprocessor may execute the following steps: detect whether disruptionsare caused and/or opportunities need to be investigated; manipulate,delete and/or add cargos according to the management preferences;calculate the cargo specific penalties for potential changes in theschedule; and feed the modified cargos with their change penalties tothe optimization model and algorithms. These steps may be provided inlogic or set of instructions as discussed further below.

The intelligent model preprocessor provides users ability to specifytheir business preference towards LNG supply chain management. Forexample, if a ship is delayed and delivery of one cargo is affected,there may be two approaches to address the problem, which may includedelaying the cargo (which is cost efficient but may reduce customer'ssatisfaction) and/or in-chartering an extra ship for that cargo (whichis expensive but may improve customer's satisfaction). The user canspecify which approach is preferred. For instance, if the ship delay isless than a certain number of days, the former may be preferred than thelatter, if a specific customer is involved, however, the latter may bepreferred than the former. As another example, if a customer requests tochange a cargo delivery date, the user can specify the amount of effortpermitted to satisfy that request. For instance, if the request is fromsome specific customer, the user may prefer to in-charter an extra shipif necessary to satisfy the request and so to improve the customer'ssatisfaction. Otherwise, the user may prefer to negotiate the deliverydate with the customer to avoid the cost for in-chartering an extraship. If the user does not specify their preferences, defaultassumptions may be applied, which may default to the typical practice inthe art.

These cargos, whose delivery schedules may be affected by the updatedinformation, may be manipulated with respect to the delivery contract,delivery ship, delivery time, and delivery quantity. If necessary, somecargos may be deleted or optional cargos may be added to lead solutionsearching. The changes associated with these manipulated cargos may beassigned with specifically designed penalties based on the user'spreferences. The manipulation and penalty scheme may be used to enhancethe optimization solver to recover feasible schedule or investigateopportunities in a more efficient manner. When updated information isprovided, the intelligent model preprocessor may detect the followings:whether the baseline schedule is disrupted; how many cargos may beaffected; how severe the disruption is; which method is preferred andhow to fix the schedule; whether new market opportunities are present;and how to take the opportunity.

For example, if a ship is delayed seven days and in-chartering an extraship (if necessary) is preferred, the system may determine that only thefirst cargo of the delayed ship needs to be handled by the in-charterand the remaining cargo can be handled by the original ship. Then, thefirst cargo may be assigned with the in-chartered ship. Thismodification could result in schedule feasibility immediately if thein-charter's available time window fits to the first cargo's deliverywindow. If there is a solution in which the usage of this in-charter isunnecessary, the model may get rid of this in-charter when minimizingdeviation from baseline schedule by assigning a large penalty for usingextra in-charters.

As another example, if the ship is only delayed two days and delayingcargos is preferred over using in-charters, the system may determinethat there are three cargos that may be delayed potentially. Then thedelivery time flexibility for these three cargos may be enlargedaccordingly so that they can still be delivered by the delayed ship.Thus, the model may recover the schedule in a more efficient mannerbecause it only has to resolve the inventory and facility conflict whenshipping resource is not a problem. However, if delivering a cargo byits original ship two days later is not preferred compared withdelivering the cargo by another ship (e.g., not in-chartered) on time,higher penalties may be assigned to delivery delays than to shipchanges. When deviation from baseline schedule is minimized, deliverydelay could then be pushed out from the schedule if such a solutionexists.

As yet another example, if a user has six divertible cargos and a marketopportunity who may take all of these six cargos. In this scenario, theoriginal schedule is still a feasible solution. The system may evaluatethe spot market opportunity first and then minimize changes if themarket opportunity is taken. Before making a decision, the user firstneeds to understand: how many cargos should be diverted; how it mayimprove economics; and what is the impact on the baseline schedule. Toachieve this objective, the intelligent model preprocessor may add sixoptional cargos with the following specifications: 1) deliverydestination is the spot market; 2) delivery ship is existing ships(e.g., not in-chartered) if they are available to deliver the spotcargos without causing too many changes; extra in-chartered shipsotherwise; 3) delivery time is calculated so that the correspondingloading time matches the loading time of each divertible cargo; 4)delivery quantity is calculated so that the corresponding loadingquantity matches the loading quantity of each divertible cargos. Thesettings of these optional cargos may be useful to avoid shippingresource conflict, inventory tank conflict and facility conflict. Byusing these settings, the optimization solver may efficiently discoverthe first solution with diversions to the spot market by searchingsolutions in the neighborhood around these cargos. And then, the solvermay enhance the economics by optimizing the aspects associated to thesecargos. After the economics are maximized, deviation from the baselineschedule is minimized by assigning large penalty for using extrain-chartered ships. Thus, the final solution is a trade-off amongeconomic pursue, change tolerance and management preferences.

Beneficially, the intelligent model preprocessor is a scheme totranslate business preferences and new and/or updated information in LNGsupply chain management to constraints and objectives in the model thatmay be useful to the model in finding feasible and more economicsolutions in a more efficient manner. This preprocessor provides amechanism for the optimization model to obtain optimality in a moreefficient manner when feasible solutions exist, or make practicalrecommendations to the user when updated information causesinfeasibility. The preprocessor also enables the optimization model toevaluate all possible solutions and balance conflicting priorities basedthe user's requests and preference in one shot. The preprocessorprovides the user with the ability to avoid unnecessary delays. Thepresent techniques are specifically designed for and applied to, but maynot be limited to, LNG supply chain optimization. However, it may beapplied to other scheduling problems in which the schedules need to beupdated based on new information and user's preferences.

As an example, a customer request may be made to change delivery. In thebaseline schedule, a cargo named as “cargoA” has been planned. Thecargo's original specifications are listed in Table 4, including cargo1D, contract, delivery ship, delivery location, delivery time, anddelivery quantity. The cargo's original allowed flexibility is alsoshown in Table 4, below.

TABLE 4 delivery is This allow allowed allowed allowed allowed deliverydelivery delivery Quantity Cargo Ship reduction increment Advance DelayID contract ship location date (MMBTU) Optional? Change? (MMBTU) (MMBTU)(days) (days) cargoA contractA ship1 locA Mar. 12, 2014 3270000 No Yes300000 300000 0 0

In this table, the cargo is not optional and this aspect has to bereflected in the updated schedule; the cargo can be delivered by otherships instead of the specified “shipl”; the cargo can have up to 300,000million British thermal units (mmbtu) more or less quantity than thedelivery quantity; and the cargo does not have any delivery timeflexibility and so has to be delivered on Mar. 12, 2014.

The cargo's updated specifications are listed in Table 5 below.

TABLE 5 delivery is This allow allowed allowed allowed allowed deliverydelivery delivery Quantity Cargo Ship reduction increment Advance DelayID contract ship location date (MMBTU) Optional? Change? (MMBTU) (MMBTU)(days) (days) cargoA contractA ship1 locA Mar. 5, 2014 3270000 No Yes300000 300000 0 7

If the customer request involves advancing the cargo by seven days(e.g., on Mar. 5, 2014), the user may have various concerns before thedecision can be made. The user's concerns may include questions, such as(i) is it possible to satisfy the request (with or without extrain-chartered ships); (ii) What should the user propose to minimize thedeviation from the baseline schedule (e.g., most undesirable changes:introduce extra in-chartered ships; undesirable but acceptable changes:deliver the cargo between Mar. 6, 2014 and Mar. 12, 2014; and/ordesirable: deliver cargoA on Mar. 5, 2014); (iii) regular changes:reassign delivery ships (not in-chartered); (iv) how may these changesaffect other deliveries; and (v) how may these changes impact economics.The intelligent model preprocessor may perform various calculations,such as cargo manipulation and/or change the penalty scheme. The cargomanipulation may include cargoA being modified to cargoA′ as in Table 5above. In this table, the delivery date may be changed to Mar. 5, 2014;and it is allowed to delay up to seven days. The change penalty schememanipulation may include adjusting the penalty scheme. That is,penalties for all cargos may be designed as noted below in the followinglogic:

-   -   Delivery location (ω_l):        -   Original destination: ω_l=0        -   Other destinations: not allowed    -   Quantity (ω_q)        -   Within specified range: ω_q=0        -   Outside specified range: ω_q=0 if the cargo is divertible;            not allowed otherwise    -   Ship(ω_s)        -   Original ship: ω_s=0        -   Other non-in-chartered ships: ω_s=1        -   Extra in-chartered ship: ω_s=100    -   Date(ω_d)        -   For cargoA′            -   Requested date: ω_d=0            -   Within specified range (Mar. 6, 2014-Mar. 12, 2014):                ω_d=(date-Mar. 5, 2014)²            -   Outside specified range (Mar. 6, 2014-Mar. 12, 2014):                not allowed        -   For other cargos            -   Original delivery date: ω_d=0            -   Within specified range: ω_d=date-original date            -   Outside specified range: not allowed    -   Penalty for each cargo: ω_l+ω_q+ω_s+ω_d    -   Total change penalty: summation of penalties for all cargos

Beneficially, the system may provide the best recommendations instead ofmerely indicating that the request is infeasible. That is, the originalbaseline plan is always feasible, which provides the optimization solverwith a solution that avoids spending large amounts of time ondemonstrating feasibility or infeasibility. Further, when the changepenalty is minimized, trade-offs are made between satisfying thecustomer's request and modifying other customers' delivery schedule. ForcargoA′, the closer the delivery date to Mar. 5, 2014, the less thepenalty, the more impact on others' schedule. The designed penaltyscheme may be useful for the optimization solver by searching towardssatisfying the customer's request because the penalty function ω_d forcargoA′ is steeper than that for other cargos. Meanwhile, the solutiondoes not have unacceptable amount of impact on others' schedule becausethe penalties for others' change may be dominant and may be pushed outduring minimization. Also, in-charters may not be preferred comparedwith the regular changes and the economics may be maximized after thederisible change tolerance is discovered.

As a result of the LNG ship rescheduling modeling, the outputs from theLNG ship rescheduling model to the user may include information that maybe utilized for negotiation and decision making. That is, the date todeliver the cargo (e.g., some day between the original delivery date andthe requested date); the impact on other's schedule; and/or the impacton economics. Accordingly, the user may reply to the customer with acounter offer based on the outputs. The user's counter offer, if thecargo cannot be advanced with 7 days, may include an offer to advancefive days instead of seven days, as an example.

Similar to the LNG ship scheduling model and noted above, the LNG shiprescheduling model may include different algorithms that are utilized tocalculate the outputs. That is, the system includes a suite of speciallydesigned algorithms consisting of five stages. These different stagesare shown in FIG. 5B. FIG. 5B is a flow chart 550 of the five stagealgorithm method for the LNG ship rescheduling model according to anexemplary embodiment of the present techniques. In stage A, as shown inblock 552, a schedule that has no constraint violations is developed.The schedule may be created to recover the schedule from disruptions(e.g., repair schedule to satisfy all hard constraints which may beviolated due to disruptions). Then, as shown in block 554, the deviationfrom baseline schedule may be minimized in stage B. Then, in stage C, asshown in block 556, economics may be maximized while sustainingdeviation at the minimized level or within a pre-specified tolerance. Atblock 558, in stage D, economics is maximized regardless the deviationfrom the baseline schedule. In this stage, new market opportunitieswhich may induce significant changes may be captured. Then, in stage E,the deviation from baseline schedule is minimized, while sustaining acertain level of economics, as shown in block 560. The output of thesealgorithms may be used to for LNG shipping operations and negotiationswith third parties, as shown in block 562.

The system may perform these algorithms stage by stage, wherein thesolution of the previous stage is automatically fed to the followingstage to speed up the optimization algorithms. Depending on the newand/or updated information, some of these stages may be bypassed orskipped. For an example, if disruptions are caused by the new and/orupdate information, while no new market opportunities are provided, thesystem may perform the algorithms in stages A to stage C and skippingstage D and stage E. As another example, if disruptions are not caused,while only new market opportunities need to be investigated, the systemcould skip performing the algorithm in stage A to stage C and performthe algorithms in stage D and stage E. When both disruptions and newmarket opportunities are present, the system can also be terminatedafter any of these stages depending on user's need.

Further, each of the stages of the algorithms solves a series of localneighborhood optimization problems. The local neighborhood structuresare designed for this system include, but are not limited to, thosedescribed below.

In stage A, the schedule is recovered from disruptions, as noted inblock 552. This stage determines a means to recover the schedule fromdisruptions in a more efficient manner (e.g., repair schedule in a moreefficient manner to satisfy all hard constraints, which may be violateddue to the new and/or updated information). In this stage, non-negativeslack variables may be added to hard constraints; and the total amountof slack variables is minimized. If the objective function value can beminimized to zero, then the schedule is recovered successfully.Otherwise, the solution may suggest what may cause infeasibility andprovide hints for the user to resolve the disruptions. This stageincludes three major steps, which include (step 1) construct a solutionbased on the cargos specified in the baseline schedule and the cargosmanipulated by the intelligent model preprocessor, (step 2) improve thesolution by applying a series of local neighborhood searches, which aresimilar as in the annual delivery model; and (step 3) search thesolution directly in the global region to demonstrate feasibility orinfeasibility if step 1 and step 2 fail to discover the first feasiblesolution.

Due to the potential disruptions caused by the new and/or updatedinformation, the baseline schedule may not be feasible. However, thecargos manipulated by the intelligent model preprocessor are designed torecover the schedule from disruptions in a more efficient manner. Thus,a narrow neighborhood is constructed and searched around the cargosspecified in the baseline schedule and the cargos manipulated by theintelligent model preprocessor. Very often, the slack variables can bepushed to zero in a more efficient manner due to the design of themanipulated cargos. Branch and bound node limits can be specified toterminate each search, as well.

If the first feasible solution is not discovered in the aboveneighborhood, local neighborhood searches may be performed in asequence. That is, k-day-flexibility search, rolling time window search,one-ship search and two-ship search may be performed in a specificsequence. These searches are similar as the searches designed for theannual delivery model, which is discussed further above. Branch andbound node limits may be specified to terminate each search.

If step 1 and step 2 fail to determine the first feasible solution, step3 may be performed either to find the first feasible solution or todemonstrate infeasibility of the original configuration and providesuggestions on how to adjust the schedule. In step 3, slack variablesmay be deleted and the global region may be searched. The search may beterminated after a time limit specified by user or specified number ofiterations. For example, if a terminal is down, the user needs to divertall cargos planned for that terminal to another candidate spot market.However, the shipping resource and inventory management conflict may notbe solved. As such, the spot market does not have the capacity toconsume all volumes that need to be diverted or the user does not haveresource to satisfy the requirement of the spot market. Thus, the usermay need to consider another market or obtain more resource.

In stage A, the system provides the capability to recover from varioustypes of disruptions. When disruptions have been pre-built in theintelligent model preprocessor, the schedule can be recovered in a moreefficient manner in step 1; otherwise, step 2 and step 3 can obtainfeasible solutions for any general type of disruptions. Also, if adisruption type is new and cannot be resolved in step 1, the intelligentmodel preprocessor may learn from that disruption, update its techniquesand develop new capabilities to recover from that type of disruption infuture performances.

In stage B, the deviation from baseline schedule is minimized, as shownin block 554. The first schedule recovered from disruptions may have anunacceptably significant amount of deviation from the baseline schedule.This stage is designed to minimize the deviation from baseline schedule.The algorithms in this stage can be categorized into two groups, whichare a group of searches which may eliminate changes for all cargossimultaneously within a local neighborhood, and another group ofsearches each of which may eliminate changes of one specific cargo.

The first group includes a time window search and two global searches.In the time window search, the changes are detected one by one startingfrom the beginning of 90-day period, and a time window around the changeis created. Within the time window, variables are freed and may bere-evaluated, while outside the time window, variables may be fixed totheir current values. In one global search, variables in the posteriorperiods are fixed to the current values and the remainders are freed up;in the other global search, decision variables related to in-charteredships are fixed to their current values and the remainders are freed up.Branch and bound node limit can be specified to terminate each search.

The second group includes a neighborhood structure, which isspecifically designed for cargos with changes. If a cargo has deviationfrom the baseline schedule, a hybrid neighborhood structure may becreated for the cargo. In this neighborhood, variables, either insidethe time window which is created around the cargo, or associated withthe decisions corresponding to the cargo, or related to the ship thatoriginally delivers the cargo in the baseline schedule, may be freed up;other variables may be fixed to their current values. Cargos can besorted according to the significance of their deviations in thisneighborhood search. For an example, cargos which are using extrain-chartered ships can be investigated earlier than the cargos whichonly have regular date or ship changes. Branch and bound node limit canbe specified to terminate each search.

If the user has higher tolerance towards the deviation from the baselineschedule, the deviation tolerance can also be used to terminate thealgorithm. That is, the algorithm can be stopped as soon as a solutionwhose deviation is within tolerance is discovered.

In stage C, the economics are maximized, while sustaining minimizeddeviation. The economics for the solution with minimized deviationdiscovered in stage B may be enhanced by optimizing options. Forexample, different speeds, fuel modes and spot markets may be some ofthe options. This stage may maximize economics among solutions with theminimized deviation or a deviation within a specified tolerance byadding an upper bound to the total deviation. The algorithms in thisstage may include two rolling time window searches with differentparameter specifications. The structure of rolling time window searchesis similar to that described above for the annual delivery model.

The first rolling time window search has more narrow windows (ascompared with the second rolling time window search). For eachiteration, all variables, either outsides of the window, or for thedecisions associated with a spot market, or for the decisions associatedwith an extra in-chartered ship, are fixed to their current values; andall other variables are freed up. This neighborhood search is directedto optimize the options which are not related to spot marketopportunities, for example the options for fuel, speed, or heel, etc. Asmaller branch and bound node limit can be specified to terminate thesearch.

The second rolling time window search has wider windows (as comparedwith the first rolling time window search). For each iteration,variables outsides of the window are fixed to their current values; andother variables are freed up. This neighborhood search may not onlyoptimize the options which are not related to spot market opportunities,it may also investigate whether cargos should be sent to spot market toimprove economics. This neighborhood is larger compared to the first oneand thus a larger branch and bound node limit should be specified toterminate the search.

This is the stage where the user can test the trade-off betweendeviations from the baseline schedule and the economic pursue. Within acertain threshold, the higher the deviation tolerance, the maximumeconomics results. Above the threshold, however, further higherdeviation tolerance may not be useful because the optimality of theproject may have been achieved.

In stage D, economics may be further maximized regardless scheduledeviation from the baseline schedule. The new optionality may beevaluated. As noted above, economics are maximized within the specifiedchange tolerance in stage C. If the change tolerance is small or set upimproperly, new market opportunities, which could further maximizeeconomics, may be excluded because they may involve significant amountof changes on the schedule. To resolve this situation, economics may bemaximized without any change limit in this stage. Rolling time windowalgorithms are designed and applied in this stage.

The rolling time window is a similar hybrid structure as in stage C.Rolling time windows are created one by one starting from the beginningof 90-day horizon. For each iteration, all variables, either inside ofthe time window, or for decisions associated with optional cargos, orfor decisions associated with spot markets, are freed up foroptimization while all others are fixed to their current values. Withoutchange limit, the optimization solver is free to take all options orspot market opportunities as long as the economics are improved. Thus,the solution in this stage has the optimal economic benefit given theresource and market at that time.

In stage E, deviations are minimized while sustaining maximizedeconomics. The schedule achieved after stage C may have unnecessarychanges or unacceptable significant amount of deviations even though theeconomics are maximized. This stage may provide the user a differentapproach to make the trade-off between deviations from baseline scheduleand the economic performance. In this stage, deviations may be minimizedwhile sustaining the economics at the maximized or a pre-specifiedlevel. The algorithms in this stage are similar as those in stage B. Theonly difference is that, a lower bound is added to the economics in thelocal neighborhood optimization problems in this stage to sustain theeconomic level.

In this stage, the user can specify how much economic benefit to beallocated to minimize deviations from baseline schedule. Within acertain threshold, the more economics given up, the less the deviationsfrom baseline schedule. Above the threshold, however, further largertolerance for economic decrement may not be beneficial because thedeviations from baseline schedule may have been minimized. The solutionachieved in stage B and stage D present the two extreme preferences thatthe user may have regarding with the trade-off between solutionreliability and economic pursue. This model provides user capability tomake selections among these solutions at the same time.

This system is designed for and applied to, but is not limited to, LNGship rescheduling optimization. The present techniques perform one ormore of the following: recovering from the disruptions; minimizingchanges to the schedule; maximizing benefits while sustaining deviationswithin a certain level; maximizing benefit by evaluating newopportunities; minimizing changes while sustaining economics at acertain level. These provide a mechanism to update schedules for allprojects in which planning assumptions are changed frequently andtrade-off between schedule reliability and economic pursue has to bemade.

As an output from the system, the system may be used in various mannersduring project-specific operations planning, such as: to negotiate anADP during the negotiation process; to develop an initial 90-daydelivery plan; and/or to evaluate the effect of customer requests formodifying the initial 90-day delivery plan. Also, it may be used torecover ship schedule from disruptions (e.g. ship breakdown, and/orterminal breakdown). Further, it may be used in a similar way for otherscheduling time horizons other than annual and 90-days. The shipschedule optimizer may be integrated with maintenance and productionplanning, as well as looking at maximizing profits by utilizingflexibility within contracts. For example, the ship schedule may beoptimized simultaneously with any of the following: LNG inventory levelsat one or more LNG liquefaction terminals; LNG inventory levels at oneor more LNG regasification terminal; fuel selection for at least onevoyage; ship speed for at least one voyage; a maritime route for atleast one voyage; berth assignment at any of the LNG liquefactionterminals and/or LNG regasification terminals; ship maintenanceschedules; and LNG liquefaction or production schedules. Further,aspects disclosed herein may be used for developing delivery plans foran individual LNG producer to one or more customers, or for a companythat owns equity gas in one or more LNG liquefaction terminals anddelivers LNG to one or more customers. Disclosed aspects may also beused to evaluate the performance of an optimized schedule under variousfuture scenarios associated with uncertainties identified herein.

The system may provide the user the capability to recover schedule fromdisruptions and evaluate new market opportunities in a more efficientmanner through its intelligent model preprocessor. Depending on howsevere the disruption is and how urgent that the schedule needs to berecovered, the system can provide different solutions with differentpriorities. If the user prefers to recover the schedule as soon aspossible regardless of deviation from the baseline schedule, the systemmay stop immediately after stage A which is designed to get the firstfeasible/practical solution in a more efficient manner. Alternatively,if the user also wants to minimize deviation from the baseline schedule,the system could be terminated after stage B in which the total weightfor prioritized changes is minimized until satisfying theuser-pre-defined change tolerance. Alternatively, if economic benefitsare also pursued, the system could run till the end of stage C in whicheconomics are maximized with the given change tolerance. Alternatively,if more market opportunities need to be investigated, the system couldperform stage D in which economics may be maximized regardless thechange tolerance and stage E in which changes may then be minimizedwhile sustaining the economic advantages.

The system may provide the user the ability to analyze the trade-offbetween minimizing changes in the updated planned compared to thebaseline plan, and maximizing the incremental economics. For example,the system may optimize economics without constraining for the number ofchanges compared to a baseline plan. The system may also develop aschedule that minimizes overall changes compared to the baseline plan.Alternatively, the system may optimize economics for a given number ofchanges compared to baseline plan. Alternatively this system mayminimize the number of changes for a given level of economicperformance. In each of these cases, the system provides a mechanism forthe user to specify the degree of flexibility and importance associatedwith each type of change on a cargo by cargo basis.

FIG. 6 is a chart 600 of a notional curve 602 representing theincremental economics generated by allowing for more changes in theupdated schedule compared to the baseline schedule. Within a certainthreshold, the higher the deviation tolerance, the maximum economicsresults, as shown on the curve 602. Above the threshold, however,further higher deviation tolerance may not be useful because theoptimality of the project may have been achieved.

The system may provide the ability to optimize economics from severalperspectives including minimizing costs, maximizing throughput,maximizing profit etc.

The updated schedule can be used as a starting point to negotiate 90-daydelivery schedules with customers. During the negotiation with aspecific customer, the system can be used to evaluate the operationalfeasibility, economic effect, and side-effects on the delivery schedulesfor the overall portfolio of satisfying requests by that specificcustomer. For example, the system may evaluate how a customer request(or the relevant scenario under consideration) may affect LNG inventoryprofiles at each storage tank farm, and re-optimize the schedule toensure that storage inventory constraints are satisfied. Similarly, thesystem may evaluate whether the relevant scenario may cause any berthconflicts and may reoptimize the schedule to mitigate such issues.Similarly, the system may evaluate if the relevant scenario may causeany contractual constraints to be violated, the system may re-optimizethe schedule to ensure that these violations are minimized

The system can also be used to reoptimize schedules during negotiationswith customers during the 90-day scheduling or ADP development stage

The system can optimize the schedule at different levels of granularity.For instance, some decisions (e.g. ship speed) may be optimized at afiner scale in the near term (e.g., within the time period of therescheduling horizon, such as within the 90-day plan), and at a coarserscale beyond the near term. Similarly, the system may optimize ship fuelselection in the near term, but assume nominal selection rules beyondthe near term.

The design of the system can be applied to any scheduling problems inwhich schedules need to updated frequently based business's preferencesand new information, and trade-off between change minimization andeconomics maximization need to be made.

As an example, the present techniques for LNG ship rescheduling mayinclude various scenarios that can be solved by this system include, butare not limited to, the followings: 1) production and inventory change(e.g., daily production rate differs from the production assumptions andor inventory level deviates from the predicted volume in the baselineschedule); 2) ship delay or ship down (e.g., ship delay by several days;and/or ship is down for a significant long period); 3) terminal down(e.g., terminal is down so cargos need to be diverted; terminal isclosed for maintenance so cargos need to be rescheduled; and/or berth isclosed for maintenance); 4) change requests from customers (e.g.,customer requests to change deliverydestination/ship/time/quantity/grade and/or negotiation about the annualdelivery plan); 5) divert contracts to spot market (e.g., spot marketthat accepts certain ship types/cargo sizes/LNG grade); 6) dry-dockwindow is moved from period to period (e.g., to ensure ample shippingresource during periods with higher LNG price) and/or 7) combinedscenarios with more than one feature specified above.

The system may also provide intuitive interfaces to provide the userflexibility and enhance the identifying the differences between thebaseline and the updated schedules. As an example, FIG. 7 is a diagram700 that represents the difference in ship schedules in the updated andbaseline plans for three ships. In this diagram 700, the difference inship schedules in the updated and baseline plans is shown by thedifferent color boxes. The diagram 700 shows the differences in the twoschedules for three ships where a schedule changed for one of itsoriginal cargos. Each voyage (e.g., laden or ballast) is represented byone box. The boxes are color-coded by cargo, as noted in the legend 702.

FIG. 8 shows a chart 800 of a notional comparison of economics for theupdated delivery plan compared to baseline delivery plan. The changesshown in FIG. 7 provide the economic benefits shown in the chart 800 ofFIG. 8.

The user interface for the system may provide several capabilities forcomparing two or more versions of the same schedule. The intended use ofthis functionality is to identify where the optimization model hasmodified the schedule in a significant way and provide an easy methodfor quantifying the value of the updated schedule.

To quantify the value of the schedule updates, the original ADP as wellas the previous version of the schedule may be benchmarked. Benchmarkingagainst the ADP provides insights into the comparison of the 90-day planand the ADP. This type of benchmarking provides insights into thequestion of “how are we doing according to the original plan?” or “howare we doing given all previous schedule modifications since the ADP?”.

Outputs from the model may be utilized to provide visual indicators forthe user. As an example, the outputs may include side-by-side comparisoncharts for certain metrics. These metrics may include total units loadedand delivered; sales revenue in dollars per unit delivered and overall;profit in dollars per unit delivered and overall; shipping cost indollars per unit delivered and overall; profit in dollars per unitdelivered and overall; burn down chart showing ADP as the baseline withprevious and current schedule to project the schedule status (e.g.,units delivered, shipping costs; sales revenue, and profit). All of theabove metrics should be displayed for the schedule overall, each JointVenture, each Contract, and each Region of sale.

When determining the value it is also valuable to have other metrics tobenchmark two solutions. Some of these examples are Ship Utilization,Number of Cargos, Ratio of the Number of Cargos to the Cargo Size, KnotsSailed, Canal Fees Paid, etc. These metrics can quantify how much theschedule has changed for the value that is seen from the above charts.

For the system to effectively communicate changes to a schedule, severalunique comparison interfaces needed to be implemented. Accordingly,displaying schedule differences side-by-side are filtered down to therelevant changes as well as cargoes that fall within the visible timeperiod as defined by the user. These changes can be viewed side-by-sideand from multiple different perspectives, as shown in FIGS. 9 and 10.

FIG. 9 is a diagram 900 that represents the difference in ship schedulesfor how cargos are moving between the ships. In this diagram 900, thechanges are represented for each individual ship.

FIG. 10 is another diagram 1000 that represents the difference in shipschedules for how cargos are moving between the ships. In this diagram1000, cargo schedule are represented as to how the cargos are changingwith respect to time, but also shows when a ship is changed.

In these diagrams 900 and 1000, the perspective blends the cargo indiagram 900 to show the customer's delivery schedule for their cargosonly. The value of this display is to provide clear indications andnotifications with a user.

The customer schedule shows how a customer's cargos are changed for thepurposes of updating the customer on the impact. To illustrate thescheme of this system, the ship down case is shown below. In thisscenario, a ship Ship1 is down in the middle of February and may getback to the production terminal on Mar. 18, 2014 instead of Mar. 11,2014 as planned. Due to the tight schedule of the ship Ship1, the delaymay affect two cargos. This aspect is shown further in the cargosplanned for ship Ship1 during March-May, which is indicated in Table 6below.

TABLE 6 slack slack days at days at produc- desti- Load tion nationCargo Vessel Ter- Load Arrive Cus- Desti- Unload Back Back termi- termi-Name Name minal Date Date tomer nation Date Date Terminal nal nal Ship1Cstm Dest Feb. 27, 2014 Mar. 11, 2014 ProdTr Cargo1 Ship1 ProdTr Mar.11, 2014 Mar. 22, 2014 Cstm Dest Mar. 24, 2014 Apr. 5, 2014 ProdTr  0 2Cargo6 Ship1 ProdTr Apr. 6, 2014 Apr. 17, 2014 Cstm Dest Apr. 19, 2014May 1, 2014 ProdTr  1 2 Cargo7 Ship1 ProdTr May 4, 2014 May 15, 2014Cstm Dest May 16, 2014 May 28, 2014 ProdTr  3 1 Cargo8 Ship1 ProdTr Jun.10, 2014 Jun. 21, 2014 Cstm Dest Jun. 22, 2014 Jul. 4, 2014 ProdTr 13 1

As shown in the Table.6, when the ship Ship1 is delayed from Mar. 11,2014 to Mar. 18, 2014, the cargos Cargo1 and Cargo6 are impacted andcargo Cargo7 may or may not be affected depending on the change offacility usage caused by the ship being down. The system may repair theschedules for March to December (March-December) with minimum changegiven that the customers do not permit any delivery side change afterJuly and allow ship change, 1 day delivery change and 10% of quantitychange during March-May period.

The system is able to recover the schedule in 15 minutes with minimizedtotal weight of changes and maximized economics. An extra in-charteredship is not used as in this correction and five cargos shipreassignments. After the schedule is repaired, the economics areimproved by further optimizing fuel and speed options. Table 7 describesthe cargos ship reassignment for the baseline schedule versus therepaired schedule or plan.

TABLE 7 Baseline Schedule Repaired Plan cargoID ship Load Date UnloadDate ship Load Date Unload Date Cargo1 Ship1 Mar. 11, 2014 Mar. 24, 2014-> Ship3 Mar. 11, 2014 Mar. 24, 2014 Cargo2 Ship4 Mar. 17, 2014 Mar. 29,2014 -> Ship1 Mar. 18, 2014 Mar. 29, 2014 Cargo5 Ship3 Mar. 17, 2014Apr. 2, 2014 -> Ship4 Mar. 17, 2014 Apr. 2, 2014 Cargo3 Ship1 Apr. 6,2014 Apr. 19, 2014 -> Ship2 Apr. 7, 2014 Apr. 19, 2014 Cargo4 Ship2 Apr.11, 2014 Apr. 23, 2014 -> Ship1 Apr. 11, 2014 Apr. 23, 2014

FIG. 11 is another diagram 1100 that represents the difference in shipassignments.

FIG. 12 is another diagram 1200 that represents the difference in shipassignments.

See FIG. 11 and FIG. 12 for the illustration of ship reassignments atbelow.

As an example, FIG. 13 is a block diagram of a computer system 1300 thatmay be used to perform any of the methods disclosed herein. A centralprocessing unit (CPU) 1302 is coupled to system bus 1304. The CPU 1302may be any general-purpose CPU, although other types of architectures ofCPU 1302 (or other components of exemplary system 1300) may be used aslong as CPU 1302 (and other components of system 1300) supports theinventive operations as described herein. The CPU 1302 may execute thevarious logical instructions according to disclosed aspects andmethodologies. For example, the CPU 1302 may execute machine-levelinstructions for performing processing according to aspects andmethodologies disclosed herein.

The computer system 1300 may also include computer components such as arandom access memory (RAM) 1306, which may be SRAM, DRAM, SDRAM, or thelike. The computer system 1300 may also include read-only memory (ROM)1308, which may be PROM, EPROM, EEPROM, or the like. RAM 1306 and ROM1308 hold user and system data and programs, as is known in the art. Thecomputer system 1300 may also include an input/output (I/O) adapter1310, a communications adapter 1322, a user interface adapter 1324, anda display adapter 1318. The I/O adapter 1310, the user interface adapter1324, and/or communications adapter 1322 may, in certain aspects andtechniques, enable a user to interact with computer system 1300 to inputinformation.

The I/O adapter 1310 preferably connects a storage device(s) 1312, suchas one or more of hard drive, compact disc (CD) drive, floppy diskdrive, tape drive, etc. to computer system 1300. The storage device(s)may be used when RAM 1306 is insufficient for the memory requirementsassociated with storing data for operations of embodiments of thepresent techniques. The data storage of the computer system 1300 may beused for storing information and/or other data used or generated asdisclosed herein. The communications adapter 1322 may couple thecomputer system 1300 to a network (not shown), which may enableinformation to be input to and/or output from system 1300 via thenetwork (for example, a wide-area network, a local-area network, awireless network, any combination of the foregoing). User interfaceadapter 1324 couples user input devices, such as a keyboard 1328, apointing device 1326, and the like, to computer system 1300. The displayadapter 1318 is driven by the CPU 1302 to control, through a displaydriver 1316, the display on a display device 1320. Information and/orrepresentations of one or more 2D canvases and one or more 3D windowsmay be displayed, according to disclosed aspects and methodologies.

The architecture of system 1300 may be varied as desired. For example,any suitable processor-based device may be used, including withoutlimitation personal computers, laptop computers, computer workstations,and multi-processor servers. Moreover, embodiments may be implemented onapplication specific integrated circuits (ASICs) or very large scaleintegrated (VLSI) circuits. In fact, persons of ordinary skill in theart may use any number of suitable structures capable of executinglogical operations according to the embodiments.

In one or more embodiments, the method may be implemented inmachine-readable logic, set of instructions or code that, when executed,performs the instructions to create the 90-day plan and/or the ADP. Themethod may perform the operations noted in the blocks of the differentfigures.

In one or more embodiments of LNG ship scheduling model, the LNG shipscheduling model may include various logic and components. The systemmay be used to develops annual delivery plan given the contractspecification; production and re-gasification location specifications,rates; ship specifications; ship compatibilities with contracts,production and re-gas terminals, berths; dry-dock requirements; storagetank requirements; berth specifications; berth and storage maintenance;loans/transfers of LNG among co-located JVs; LNG grades; optionalityopportunities including in-charter, out-charter, diversion; priceprojections; and/or potential ship routes. The system generatesdecisions, which are based on ship travel details (time, route, fuel,speed, “state” (warm vs. cold)); cargo size, LNG quality; storage-berthcombination which a cargo is served; is ship in-chartered orout-chartered; and/or does ship go to dry-dock on a certain day. Thesystem may also optimize decisions in consideration of ship travelrestrictions based on arrival and departure times, speed, distance,loading/unloading operations; compatibility requirements between JV,storage tanks, berths, contracts, cargo sizes; limits on loans betweenJV's; storage restrictions; berth utilization; contractual requirements(quantity, time); and/or maintenance requirements. The system may alsooptimize decisions to maximize/minimize an objective; maximize socialinterest; minimize shipping costs; maximize joint venture profitability(single JV case); maximize IOC shareholder profitability (multiple JVcase); maximize NOC interest (multiple JV case). The system usesalgorithms to optimize model may include basic algorithm has five stagesor phases, as noted above. Further, the system may include extensions tohandle soft constraints, such as soft ratability constraints (approach1: minimize violation after Stage 5 and approach 2: minimize violationafter stage 2) and/or soft inventory constraints (e.g., approach 1:minimize violation after stage 5 and approach 2: minimize violationafter stage 2.

In one or more embodiments of the LNG ship rescheduling model, LNG shiprescheduling model may include various logic and components. The systemmay develop updated LNG loading and delivery schedules. The presenttechniques may adjust the given baseline schedule for individualflexibility specifications towards cargo divertible/optional, deliverydestination; delivery ship, delivery date, and/or delivery quantity. Inresponse to new/updated information, the system may update the schedulein response to updated planning information; disruptions; new marketopportunities; new customer request etc. The objective of the system maybe to minimize changes; maximize economics; and/or combined (balancechanges and economics). The LNG ship rescheduling model may provideadjustment to the length of planning horizon is adjustable; posteriorhorizon further in future; degree of flexibility (# decision variables)in different horizon. The system may include various constraints forcargos, such as a cargo is enforced if it is not divertible/optional;cargos cannot have changes beyond their specified flexibilities;cargo-based penalties are assigned to each cargos to penalize changes;and/or the total penalties are minimized. As part of the system, anintelligent model preprocessor may be provided, which provide user'spreferences responding to new/updated information; cargo manipulation;penalty calculation and/or guide optimizations to recover feasibleschedules and achieve better economics in a more efficient manner. Thesystem may also provide balancing between schedules need to updatedbased business's preferences and new information and/or trade-offbetween change minimization and economics maximization need to be made

The disclosed aspects, methodologies and techniques may be susceptibleto various modifications, and alternative forms and have been shown onlyby way of example. The disclosed aspects, methodologies and techniquesare not intended to be limited to the specifics of what is disclosedherein, but include all alternatives, modifications, and equivalentsfalling within the spirit and scope of the appended claims.

What is claimed is:
 1. A computer implemented method for generating anoptimized ship schedule to deliver liquefied natural gas (LNG) from oneor more LNG liquefaction terminals to one or more LNG regasificationterminals using a fleet of ships, comprising: obtaining a baseline shipschedule for LNG shipping operations; obtaining input data associatedwith the LNG shipping operations; in response to the input data,developing an updated ship schedule using one or more algorithms,wherein the one or more algorithms balance the economics of the updatedship schedule and the changes in the updated ship schedule as comparedto the baseline ship schedule; outputting one or more notificationsassociated with the updated ship schedule; and using the one or morenotifications to perform LNG shipping operations.
 2. The computerimplemented method of claim 1, wherein the one or more algorithmsprovide flexibility in specifications in one or more of deliverydestination, delivery ship, delivery date, and delivery quantity.
 3. Thecomputer implemented method of any one of claims 1 to 2, wherein the oneor more notifications provide visual indications of changes between thebaseline ship schedule and the updated ship schedule.
 4. The computerimplemented method of any one of claims 1 to 3, wherein the input datacomprises a disruption to the baseline ship schedule.
 5. The computerimplemented method of any one of claims 1 to 4, wherein the input datacomprises new market opportunities.
 6. The computer implemented methodof any one of claims 1 to 5, wherein developing an updated ship scheduleusing one or more algorithms comprises developing an updated shipschedule that does not have violations.
 7. The computer implementedmethod of any one of claims 1 to 6, wherein developing an updated shipschedule using one or more algorithms comprises minimizing deviationbetween the updated ship schedule and the baseline ship schedule.
 8. Thecomputer implemented method of any one of claims 1 to 7, whereindeveloping an updated ship schedule using one or more algorithmscomprises maximizing economics while maintaining deviation between theupdated ship schedule and the baseline ship schedule within a tolerance.9. The computer implemented method of any one of claims 1 to 8, whereindeveloping an updated ship schedule using one or more algorithmscomprises evaluating additional LNG market opportunities.
 10. Thecomputer implemented method of any one of claims 1 to 9, whereindeveloping an updated ship schedule using one or more algorithmscomprises minimizing deviation between the updated ship schedule and thebaseline ship schedule while maintaining the economics.
 11. The computerimplemented method of any one of claims 1 to 5, wherein developing anupdated ship schedule using one or more algorithms comprises one or moreof: developing an updated ship schedule that does not have constraintviolations; minimizing deviation between the updated ship schedule andthe baseline ship schedule; maximizing economics while maintainingdeviation between the updated ship schedule and the baseline shipschedule within a tolerance; maximizing economics; and minimizingdeviation between the updated ship schedule and the baseline shipschedule while maintaining the economics.
 12. The computer implementedmethod of any one of claims 1 to 11, wherein developing an updated shipschedule using one or more algorithms comprises adjusting the timeperiod for the updated ship schedule.
 13. The computer implementedmethod of any one of claims 1 to 1, wherein developing an updated shipschedule using one or more algorithms comprises using an intelligentmodel preprocessor to translate business preferences and updated inputdata to constraints and objectives in the model to find feasible andeconomic solutions in a more efficient manner.
 14. The computerimplemented method of claim 13, wherein the intelligent modelpreprocessor provides flexibility in manipulating individual cargo. 15.The computer implemented method of claim 14, wherein the intelligentmodel preprocessor provides a penalty scheme associated with theindividual cargo.
 16. The computer implemented method of any one ofclaims 1 to 24, wherein the input data comprises new customer request.17. A system for generating an optimized ship schedule to deliverliquefied natural gas (LNG) from one or more LNG liquefaction terminalsto one or more LNG regasification terminals using a fleet of ships,comprising: a processor; an input device in communication with theprocessor and configured to receive input data associated with the LNGshipping operations; memory in communication with the processor, thememory having a set of instructions, wherein the set of instructions,when executed, are configured to: obtain a baseline ship schedule forLNG shipping operations; in response to the input data, develop anupdated ship schedule using one or more algorithms, wherein the one ormore algorithms balance the economics of the updated ship schedule andthe changes in the updated ship schedule as compared to the baselineship schedule; an output device that outputs one or more notificationsassociated with the updated ship schedule.
 18. The system of claim 17,wherein the set of instructions are further configured to develop anupdated ship schedule that does not have violations.
 19. The system ofany one of claims 17 to 20, wherein the set of instructions are furtherconfigured to minimize deviation between the updated ship schedule andthe baseline ship schedule.
 20. The system of any one of claims 17 to19, wherein the set of instructions are further configured to maximizeeconomics while maintaining deviation between the updated ship scheduleand the baseline ship schedule within a tolerance.
 21. The system of anyone of claims 17 to 20, wherein the set of instructions are furtherconfigured to evaluate additional LNG shipment opportunities.
 22. Thesystem of any one of claims 17 to 21, wherein the set of instructionsare further configured to minimize deviation between the updated shipschedule and the baseline ship schedule while maintaining the economics.